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Cited by 173Neural Networks and ApplicationsPsychometric Methodologies and TestingStatistical Methods and Applications

The DOI matches its Crossref record; 105 of the 105 references it cites were found in a registry or an index.

Hybrid artificial neural network and structural equation modelling techniques: a survey

Open access
Albahri, A. S., Alnoor, Alhamzah, Zaidan, A. A., Albahri, O. S., Hameed, Hamsa, Zaidan, B. B., Peh, S. S., Zain, A. B., Siraj, S. B., Masnan, A. H. B., Yass, A. A.·2022·Complex & Intelligent Systems
DOI 10.1007/s40747-021-00503-wPMC PMC8402975
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Abstract

Abstract 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.

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Crossref · 10.1007/s40747-021-00503-w

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Figures

Fig. 1 Flowchart of the processes of classifying, screening and including relevant studies
Fig. 1 Flowchart of the processes of classifying, screening and including relevant studiesCC BY · PubMed Central
Fig. 2 Taxonomy of literature on SEM–ANN
Fig. 2 Taxonomy of literature on SEM–ANNCC BY · PubMed Central
Fig. 3 Number of articles by category and publication journal
Fig. 3 Number of articles by category and publication journalCC BY · PubMed Central
Fig. 4 Most relevant sources
Fig. 4 Most relevant sourcesCC BY · PubMed Central
Fig. 5 Country-specific production
Fig. 5 Country-specific productionCC BY · PubMed Central
Fig. 6 Annual production
Fig. 6 Annual productionCC BY · PubMed Central
Fig. 7 Most frequent words
Fig. 7 Most frequent wordsCC BY · PubMed Central
Fig. 8 Categories of the benefits of combining SEM with ANN
Fig. 8 Categories of the benefits of combining SEM with ANNCC BY · PubMed Central
Fig. 9 Challenges and limitations of SEM–ANN studies
Fig. 9 Challenges and limitations of SEM–ANN studiesCC BY · PubMed Central
Fig. 10 Recommendation categories for SEM–ANN studies
Fig. 10 Recommendation categories for SEM–ANN studiesCC BY · PubMed Central

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Research works printed but not linked: 1

  1. Sharma A (2020) A Structural equation modelling & artificial neural network approach to examine the impact of human resources accounting on firm’s performance. Pac Bus Rev Int 12(9):1–12

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Metadata

Authors
Albahri, A. S., Alnoor, A., Zaidan, A. A., Albahri, O. S., Hameed, H., Zaidan, B. B., Peh, S. S., Zain, A. B., Siraj, S. B., Masnan, A. H. B., & Yass, A. A.
Affiliations
  • Sultan Idris Education University (MY)
  • Southern Technical University (IQ)
Corresponding author
Hameed, Hamsa
ORCID
Venue
Complex & Intelligent Systems
Publisher
Springer Science and Business Media LLC
Published
2021-08-28
Volume
8
Issue
2
Pages
1781-1801
ISSN
  • 2198-6053 (ISSN-L)
  • 2199-4536
DOI
10.1007/s40747-021-00503-w
PMC
PMC8402975
OpenAlex
W3197668014
Cited by
173
References
105
Citations by year
2026: 59 · 2025: 38 · 2024: 29 · 2023: 28 · 2022: 19
FWCI
6.69
Type
journal article
Language
en
Open access
Licence
Locations
  • Complex & Intelligent Systems (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Greater South Information System
Funders
UPSi (2020-0159-106-01.)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords
classification taxonomy, children with autism, play therapy, music therapy, art therapy, health sector, technology industry, manufacturing industry

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