Based on the multi-assessment model: Towards a new context of combining the artificial neural network and structural equation modelling: A review

Abstract Theoretical models have become increasingly complex, but the dual-phase structural equation modelling (SEM) and artificial neural network analysis can be used by scholars to unveil the causal interactions and nonlinear relationships between variables. However, not only a single open issue and challenge—but several of them—are encountered in the use of different multi-assessment types of measurement model to achieve the reliability and validity whilst implementing SEM, but the gaps have not been fully determined at present. The issues significantly impact the effectiveness process of selecting the most suitable method to assess the measurement model of SEM. Once the best sequence quality improvement is met, it then needs to present a recommendable solution. To this end, this study completes the literature by presenting a systematic review of all main advanced aspects of the SEM reliability and validity approaches. Firstly, the databases of ScienceDirect, IEEE Xplore, Web of Science and Scopus were checked for the retrospective studies. A total of 239 papers were gathered for the period covering 2016 to June 2021. Then, the obtained articles were filtered according to the predefined inclusion criteria. Sixty articles were ultimately selected and divided into three categories (single, hybrid and other types) to enable a new representation of the crossover taxonomy amongst ‘SEM reliability and validity’ and ‘multi-assessment methods for structural model’ for the first time. The three categories had been matched with the SEM processes, and each of the detailed models were defined to determine the sets of principal criteria of the entire selected SEM approaches. Consequently, this multi-field interdisciplinary review was used to expose the state-of-the-art challenges and open issues (i.e. multiple-evaluation criteria, importance criteria and data variation) related to the sets of SEM criteria necessitating a selection process for deriving the best SEM method. Each issue entailed a ‘wherefore’, and multi-criteria decision making was adopted to handle the complexity problems in the different cases. Thus, a new three-phase decision-making methodology was constructed. In the first phase, a decision matrix (DM) was identified for the SEM approach; the composition of the decision alternatives and identified criteria were derived from the academic literature. In the second phase, the development methodology was achieved on the basis of the integrated multi-criteria DM techniques. The analytic hierarchy process was used for the subjective weighting of the criteria within the constructed DM, whereas the vlsekriterijumska optimizcija i kaompromisno resenje technique was used for ranking and selecting the best SEM methods. In the third phase, an objective validation approach was adopted to validate the proposed methodology. The outcome of this novel approach is intended to guide decision makers and policymakers on the easy evaluation of their goals of selecting the most suitable computing methods and the improvement of the reliability and validity of SEM.