Topical treatments with structural equation modelling (SEM) and an artificial neural network (ANN), including a wide range of concepts, benefits, challenges and anxieties, have emerged in various fields and are becoming increasingly important. Although SEM can determine relationships amongst unobserved constructs (i.e. independent, mediator, moderator, control and dependent variables), it is insufficient for providing non-compensatory relationships amongst constructs. In contrast with previous studies, a newly proposed methodology that involves a dual-stage analysis of SEM and ANN was performed to provide linear and non-compensatory relationships amongst constructs. Consequently, numerous distinct types of studies in diverse sectors have conducted hybrid SEM–ANN analysis. Accordingly, the current work supplements the academic literature with a systematic review that includes all major SEM–ANN techniques used in 11 industries published in the past 6 years. This study presents a state-of-the-art SEM–ANN classification taxonomy based on industries and compares the effort in various domains to that classification. To achieve this objective, we examined the Web of Science, ScienceDirect, Scopus and IEEE Xplore^® databases to retrieve 239 articles from 2016 to 2021. The obtained articles were filtered on the basis of inclusion criteria, and 60 studies were selected and classified under 11 categories. This multi-field systematic study uncovered new research possibilities, motivations, challenges, limitations and recommendations that must be addressed for the synergistic integration of multidisciplinary studies. It contributed two points of potential future work resulting from the developed taxonomy. First, the importance of the determinants of play, musical and art therapy adoption amongst autistic children within the healthcare sector is the most important consideration for future investigations. In this context, the second potential future work can use SEM–ANN to determine the barriers to adopting sensing-enhanced therapy amongst autistic children to satisfy the recommendations provided by the healthcare sector. The analysis indicates that the manufacturing and technology sectors have conducted the most number of investigations, whereas the construction and small- and medium-sized enterprise sectors have conducted the least. This study will provide a helpful reference to academics and practitioners by providing guidance and insightful knowledge for future studies.

使用结构方程模型 (SEM) 和人工神经网络 (ANN) 的局部治疗，包括范围广泛的概念、益处、挑战和焦虑，已经出现在各个领域并且变得越来越重要。尽管 SEM 可以确定未观察到的结构（即独立、中介、调节、控制和因变量）之间的关系，但它不足以提供结构之间的非补偿关系。与以前的研究相比，新提出的方法涉及 SEM 和 ANN 的双阶段分析，以提供结构之间的线性和非补偿关系。因此，不同部门的许多不同类型的研究都进行了混合 SEM-ANN 分析。因此，目前的工作通过系统回顾补充了学术文献，其中包括过去 6 年发表的 11 个行业中使用的所有主要 SEM-ANN 技术。本研究提出了基于行业的最先进的 SEM-ANN 分类法，并将各个领域的工作与该分类进行了比较。为了实现这一目标，我们检查了 Web of Science、ScienceDirect、Scopus 和 IEEE探索^®数据库检索 2016 年至 2021 年的 239 篇文章。根据纳入标准对获得的文章进行筛选，筛选出 60 篇研究并归入 11 个类别。这项多领域系统研究揭示了新的研究可能性、动机、挑战、局限性和建议，这些是多学科研究协同整合所必须解决的问题。它为发达的分类法带来的未来潜在工作贡献了两点。首先，医疗保健部门内自闭症儿童采用游戏、音乐和艺术疗法的决定因素的重要性是未来调查的最重要考虑因素。在这种情况下，第二个潜在的未来工作可以使用 SEM–ANN 来确定在自闭症儿童中采用感知增强疗法的障碍，以满足医疗保健部门提供的建议。分析表明，制造业和科技部门进行的调查次数最多，而建筑和中小企业部门进行的调查最少。本研究将为学术界和从业者提供有益的参考，为未来的研究提供指导和有见地的知识。