Assessing safety at work using an adaptive neuro-fuzzy inference system (ANFIS) approach aided by partial least squares structural equation modeling (PLS-SEM)

Abstract

The main objective of this research was to apply an adaptive neuro-fuzzy inference system (ANFIS) approach aided by Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess safety at work, defined as employee propensity to follow safety regulations, including safe work practices at the workplace. A survey with seven main components: 1) use of mobile technology, 2) tacit safety knowledge, 3) explicit safety knowledge, 4) attitudes toward safety: psychological aspects, 5) attitudes toward safety: emotional aspects, 6) safety culture: behavioral aspects, and 7) safety culture: psychological aspects, was used for this purpose. Workers from three manufacturing companies located in southeastern Poland completed a paper-based survey. PLS-SEM, combined with an adaptive neuro-fuzzy inference system (ANFIS) method, was used to develop the study model and determine its main components. The results showed that tacit safety knowledge, attitudes toward safety: psychological aspects, attitudes toward safety: emotional aspects, safety culture: behavioral aspects, safety culture: psychological aspects, and the use of mobile technology were significant factors influencing the perceived safety at work. Moreover, the results of the ANFIS modeling approach showed that behavioral aspects of safety culture were the most critical predictor of the perceived safety at work.