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

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.

doi:10.1007/s40747-021-00503-w

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

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

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Albahri, A. S., Alnoor, Alhamzah, Zaidan, A. A., Albahri, O. S., Hameed, Hamsa, Zaidan, B. B., Peh, S. S., Zain, A. B. +3 more·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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ANN approximates the nonlinear function between inputs and outputs with high accuracy using historical data.
“Using historical data, it approximates the nonlinear function between inputs and outputs with high accuracy [245-247].”
From Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review (2023) · cites this paper as [246]
The Introduction (page unavailable) says ANN captures nonlinear correlations among variables and provides more accurate results. It does not specifically say that ANN approximates an input–output function using historical data, or establish the unqualified claim of high accuracy. (LLM, conf=0.83)
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Multi-Criteria Evaluation and Benchmarking for Active Queue Management Methods: Open Issues, Challenges and Recommended Pathway Solutions
Khatari, M., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., & Alsalem, M. A. (2019) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622019300039 · Cited by 64 · see citing works
Comprehensive bibliometric mapping of publication trends in the development of Building Sustainability Assessment Systems
Lazar, N., & Chithra, K. (2020) · Environment Development and Sustainability · doi:10.1007/s10668-020-00796-w · Cited by 63 · see citing works
A study on the factors affecting household solar adoption in Kerala, India
Parsad, C., Mittal, S., & Krishnankutty, R. (2020) · International Journal of Productivity and Performance Management · doi:10.1108/ijppm-11-2019-0544 · Cited by 60 · see citing works
Comprehensive Insights Into the Criteria of Student Performance in Various Educational Domains
Zughoul, O., Faiez, M., Almasri, O., Zaidan, A. A., Zaidan, B. B., Alsalem, M. A., Albahri, O. S., Albahri, A. S., & Hashim, M. (2018) · IEEE Access · doi:10.1109/access.2018.2881282 · Cited by 59 · see citing works
Understanding consumer participation in managing ICT waste: Findings from two-staged Structural Equation Modeling–Artificial Neural Network approach
Najmi, A., Kanapathy, K., & Aziz, A. A. (2020) · Environmental Science and Pollution Research · doi:10.1007/s11356-020-11675-2 · Cited by 59 · see citing works
Real-Time Remote Health Monitoring Systems Using Body Sensor Information and Finger Vein Biometric Verification: A Multi-Layer Systematic Review
Mohsin, A. H., Zaidan, A. A., Zaidan, B. B., Albahri, A. S., Albahri, O. S., Alsalem, M. A., & Mohammed, K. I. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1104-5 · Cited by 58 · see citing works
Predicting the adoption of a mobile government security response system from the user's perspective: An application of the artificial neural network approach
Shahzad, F., Xiu, G., Khan, M. A. S., & Shahbaz, M. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101278 · Cited by 58 · see citing works
Real-Time Medical Systems Based on Human Biometric Steganography: a Systematic Review
Mohsin, A. H., Zaidan, A. A., Zaidan, B. B., Ariffin, S. A., Albahri, O. S., Albahri, A. S., Alsalem, M. A., Mohammed, K. I., & Hashim, M. S. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1103-6 · Cited by 56 · see citing works
M-learning adoption of management students’: A case of India
Shukla, S. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10271-8 · Cited by 56 · see citing works
A proposed adoption model for green IT in manufacturing industries
Asadi, S., Nilashi, M., Samad, S., Rupani, P. F., Kamyab, H., & Abdullah, R. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.126629 · Cited by 55 · see citing works
Finger Vein Biometrics: Taxonomy Analysis, Open Challenges, Future Directions, and Recommended Solution for Decentralised Network Architectures
Mohsin, A. H., Jalood, N. S., Baqer, M. J., Alamoodi, A. H., Almahdi, E. M. A., Albahri, A. S., Alsalem, M. A., Mohammed, K. I., Ameen, H. A., Garfan, S., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2964788 · Cited by 51 · see citing works
Corporate Social Responsibility in Higher Education: A PLS-SEM Neural Network Approach
Binsawad, M. H. (2020) · IEEE Access · doi:10.1109/access.2020.2972225 · Cited by 51 · see citing works
A systematic review into the assessment of medical apps: motivations, challenges, recommendations and methodological aspect
Alamoodi, A., Garfan, S., Zaidan, B. B., Zaidan, A., Shuwandy, M. L., Alaa, M., Alsalem, M., Mohammed, A., Aleesa, A. M., Albahri, O., et al. (2020) · Health technology · doi:10.1007/s12553-020-00451-4 · Cited by 51 · see citing works
mHealth Authentication Approach Based 3D Touchscreen and Microphone Sensors for Real-Time Remote Healthcare Monitoring System: Comprehensive Review, Open Issues and Methodological Aspects
Shuwandy, M. L., Zaidan, B. B., Zaidan, A. A., Albahri, A. S., Alamoodi, A. H., Albahri, O. S., & Alazab, M. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100300 · Cited by 51 · see citing works
Exploring consumer participation in environment management: Findings from two‐staged structural equation modelling‐artificial neural network approach
Najmi, A., Kanapathy, K., & Aziz, A. A. (2020) · Corporate Social Responsibility and Environmental Management · doi:10.1002/csr.2041 · Cited by 49 · see citing works
Sensory integration therapy versus usual care for sensory processing difficulties in autism spectrum disorder in children: study protocol for a pragmatic randomised controlled trial
Randell, E., McNamara, R. L., Delport, S., Busse, M. E., Hastings, R. P., Gillespie, D., Williams‐Thomas, R., Brookes‐Howell, L., Romeo, R., Boadu, J., et al. (2019) · Trials · doi:10.1186/s13063-019-3205-y · Cited by 45 · see citing works
Based Medical Systems for Patient’s Authentication: Towards a New Verification Secure Framework Using CIA Standard
Mohsin, A. H., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Albahri, A. S., Alsalem, M. A., & Mohammed, K. I. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1264-y · Cited by 42 · see citing works
Predicting Sustainable Farm Performance—Using Hybrid Structural Equation Modelling with an Artificial Neural Network Approach
Hayat, N., Mamun, A. A., Nasir, N. A. M., Selvachandran, G., Nawi, N. C., & Quek, S. G. (2020) · Land · doi:10.3390/land9090289 · Cited by 42 · see citing works
Play, Playfulness, and Self-Efficacy: Parental Experiences with Children on the Autism Spectrum
Román‐Oyola, R., Figueroa-Feliciano, V., Torres-Martínez, Y., Torres-Vélez, J., Encarnación-Pizarro, K., Fragoso-Pagán, S., & Torres-Colón, L. (2018) · Occupational Therapy International · doi:10.1155/2018/4636780 · Cited by 41 · cited 1× · see citing works
“Autism spectrum studies have confirmed that play is an important tool for parent–child connection [100].”
Background
A two-phased SEM-neural network approach for consumer preference analysis
Chen, H., Liu, H., Chu, X., Zhang, L., & Yan, B. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101156 · Cited by 38 · see citing works
Performance Evaluation of Time-Frequency Distributions for ECG Signal Analysis
Hussein, A. F., Hashim, S. J., Aziz, A. F. A., Rokhani, F. Z., & Adnan, W. A. W. (2017) · Journal of Medical Systems · doi:10.1007/s10916-017-0871-8 · Cited by 36 · see citing works
Universal versus contextual effects on TQM: a triangulation study using neural networks
Sila, I., & Walczak, S. M. (2017) · Production Planning & Control · doi:10.1080/09537287.2017.1296598 · Cited by 34 · see citing works
Sensing-Enhanced Therapy System for Assessing Children With Autism Spectrum Disorders: A Feasibility Study
Cai, H., Fang, Y., Ju, Z., Costescu, C. A., David, D. O., Billing, E., Tom Ziemke, B., Thill, S., Belpaeme, T., Vanderborght, B., et al. (2018) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2877662 · Cited by 33 · see citing works
Impact of Information Technology on Business Performance: Integrated Structural Equation Modeling and Artificial Neural Network Approach
Şahin, H., & Topal, B. (2018) · Scientia Iranica · doi:10.24200/sci.2018.20526 · Cited by 29 · cited 1× · see citing works
“Consequently, the use of IT on the performance of supply chain operations was analysed [78].”
Method
Prediction of the Influential Factors on Eating Behaviors: A Hybrid Model of Structural Equation Modelling-Artificial Neural Networks
Kheirollahpour, M., Danaee, M., Merican, A. F., & Shariff, A. A. (2020) · The Scientific World JOURNAL · doi:10.1155/2020/4194293 · Cited by 27 · see citing works
Methodological development for selection of significant predictors explaining fatal road accidents
Dadashova, B., Ramírez, B. A., McWilliams, J. M. M., & Aparicio-Izquierdo, F. (2016) · Accident Analysis & Prevention · doi:10.1016/j.aap.2016.02.003 · Cited by 26 · see citing works
The incident effects of supply chain and cloud computing integration on the business performance
Raut, R. D., Priyadarshinee, P., Gardas, B. B., Narkhede, B. E., & Nehete, R. S. (2018) · Benchmarking An International Journal · doi:10.1108/bij-07-2017-0170 · Cited by 21 · cited 6× · see citing works
“However, further studies still exhibit potential because user–product interactive data can be collected and used to improve the accuracy of models [51, 71].”
BackgroundMethodResultInfluential
Predicting adoption of mobile payments from the perspective of taxi drivers
Chen, C., & Tsang, S. (2019) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2018.5437 · Cited by 20 · see citing works
Effect of Game Based Balance Exercises on Rehabilitation After Knee Surgery: A Controlled Observational Study
Morri, M., Vigna, D., Raffa, D., Donati, D. M., & Benedetti, M. G. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1271-z · Cited by 17 · see citing works
Modelling Facebook usage for collaborative learning in higher education
Tiruwa, A., Yadav, R., & Suri, P. K. (2018) · Journal of Applied Research in Higher Education · doi:10.1108/jarhe-08-2017-0088 · Cited by 15 · cited 3× · see citing works
“The results of ANN show that collaboration is the most important indicator of Facebook’s adoption for academic purposes and mHealth apps, followed by resource sharing, perceived enjoyment and perceived benefits [80, 81, 98, 99].”
BackgroundMethodInfluential
Formation path of customer engagement in virtual brand community based on back propagation neural network algorithm
Song, M., Qiao, L., & Law, R. (2020) · International Journal of Computational Science and Engineering · doi:10.1504/ijcse.2020.109405 · Cited by 12 · see citing works
Eutrophication research of Dongting Lake: an integrated ML-SEM with neural network approach
Li, X., Gao, Z., Chen, Z., Zeng, G., León, T. M., Liang, J., Li, F., Liu, W., Wu, H., Du, C., et al. (2017) · International Journal of Environment and Pollution · doi:10.1504/ijep.2017.088180 · Cited by 5 · see citing works
ARTIFICIAL NEURAL NETWORKS AND STRUCTURAL EQUATION MODELLING: AN EMPIRICAL COMPARISON TO EVALUATE BUSINESS CUSTOMER LOYALTY
Coelho, A., Moutinho, L., Hutcheson, G., & Silva, M. M. S. (2012) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789814407724_0006 · Cited by 3 · cited 2× · see citing works
“Hence, ANN analysis has become an essential method that complements PLS-SEM in recent research [88].”
BackgroundMethod
Eutrophication research of Dongting Lake: an integrated ML-SEM with neural network approach
Chen, R., Chen, Z., Zeng, G., León, T. M., Liang, J., Li, F., Du, C., Wu, H., Liu, W., Gao, Z., et al. (2017) · International Journal of Environment and Pollution · doi:10.1504/ijep.2017.10009295 · Cited by 2 · cited 2× · see citing works
“UTAUT2 was used in [64–66].”
Background

Flagged first, then by citations

Sorted by citations

What Is Quantitative Research? An Overview and Guidelines
Lim, W. M. (2024) · Australasian Marketing Journal (AMJ) · doi:10.1177/14413582241264622 · Cited by 316 · see citing works
“To mitigate this, researchers can engage in alternative analyses to maximize the insights generated, such as artificial neural network (ANN) analysis (see Albahri et al., 2022) or importance-performance map analysis (IPMA; see Streukens et al., 2017).”
Understanding and predicting the determinants of blockchain technology adoption and SMEs' performance
Bag, S., Rahman, M. S., Gupta, S., & Wood, L. C. (2022) · The International Journal of Logistics Management · doi:10.1108/ijlm-01-2022-0017 · Cited by 141 · see citing works
How Positive and Negative Electronic Word of Mouth (eWOM) Affects Customers’ Intention to Use Social Commerce? A Dual-Stage Multi Group-SEM and ANN Analysis
Alnoor, A., Tiberius, V., Atiyah, A. G., Khaw, K. W., Teh, S. Y., Chew, X., & Abbas, S. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2125610 · Cited by 123 · see citing works
“Similarly, according to the eWOM literature, there is no study investigating positive and negative eWOM factors for males and females based on linear and non-linear relationships (Albahri et al., 2021a, 2021b; Donthu et al., 2021; Filieri et al., 2021).”
BackgroundMethodInfluential
RETRACTED: Exploring sustainable healthcare: Innovations in health economics, social policy, and management
Hussain, A., Umair, M., Khan, S., Alonazi, W. B., Almutairi, S. S., & Malik, A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e33186 · Cited by 131 · see citing works
“For instance, it has been shown that policies that promote universal access to healthcare and work to reduce health inequities have a favorable effect on population health outcomes [28].”
Background
Retracted
Wireless sensor network security: A recent review based on state-of-the-art works
Faris, M., Mahmud, M. N., Salleh, M. F. M., & Alnoor, A. (2023) · International Journal of Engineering Business Management · doi:10.1177/18479790231157220 · Cited by 99 · see citing works
Circular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approach
Chew, X., Khaw, K. W., Alnoor, A., Ferasso, M., Halbusi, H. A., & Muhsen, Y. R. (2023) · Environmental Science and Pollution Research · doi:10.1007/s11356-023-26677-z · Cited by 104 · see citing works
From likes to loyalty: Exploring the impact of influencer credibility on purchase intentions in TikTok
Alcántara‐Pilar, J. M., Rodríguez-López, M. E., Kalinić, Z., & Liébana-Cabanillas, F. J. (2024) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2024.103709 · Cited by 152 · see citing works
Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review
Keshmiry, A., Hassani, S., Mousavi, M., & Dackermann, U. (2023) · Buildings · doi:10.3390/buildings13040918 · Cited by 96 · see citing works
Sustainable Digital Marketing Under Big Data: An AI Random Forest Model Approach
Jin, K., Zhong, Z., & Zhao, E. Y. (2024) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2023.3348991 · Cited by 84 · see citing works
The effect of social media influencer marketing on sustainable food purchase: Perspectives from multi-group SEM and ANN analysis
Wu, Y., Yang, S., & Liu, D. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.137890 · Cited by 93 · see citing works
Uncovering the antecedents of trust in social commerce: an application of the non-linear artificial neural network approach
Alnoor, A., Al‐Abrrow, H., Halbusi, H. A., Khaw, K. W., Chew, X., Al‐Maatoq, M., & Alharbi, R. K. (2022) · Competitiveness Review An International Business Journal incorporating Journal of Global Competitiveness · doi:10.1108/cr-04-2021-0051 · Cited by 82 · see citing works
Predicting Adoption Intention of Artificial Intelligence ChatGPT
Emon, M. M. H., Hassan, F., Nahid, M. H., & Rattanawiboonsom, V. (2023) · AIUB Journal of Science and Engineering (AJSE) · doi:10.53799/ajse.v22i2.797 · Cited by 70 · see citing works
Leadership styles and sustainable organizational energy in family business: modeling non-compensatory and nonlinear relationships
Abdullah, H. O., Atshan, N. A., Al‐Abrrow, H., Alnoor, A., Valeri, M., & Bayram, G. E. (2022) · Journal of Family Business Management · doi:10.1108/jfbm-09-2022-0113 · Cited by 59 · see citing works
Triage and priority-based healthcare diagnosis using artificial intelligence for autism spectrum disorder and gene contribution: A systematic review
Joudar, S. S., Albahri, A. S., & Hamid, R. A. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105553 · Cited by 58 · see citing works
Determinants of Generative AI in Promoting Green Purchasing Behavior: A Hybrid Partial Least Squares–Artificial Neural Network Approach
Foroughi, B., Naghmeh‐Abbaspour, B., Wen, J., Ghobakhloo, M., Al‐Emran, M., & Al-Sharafi, M. A. (2025) · Business Strategy and the Environment · doi:10.1002/bse.4186 · Cited by 56 · see citing works
Unveiling the Determinants of Digital Strategy from the Perspective of Entrepreneurial Orientation Theory: A Two-Stage SEM-ANN Approach
Alnoor, A., Atiyah, A. G., & Abbas, S. (2024) · Global Journal of Flexible Systems Management · doi:10.1007/s40171-024-00385-0 · Cited by 44 · see citing works
“…use of such a hybrid approach contributes to reveal causal relationships and identifying effect of relationships between variables in addition to revealing non-compensatory relationships that determine the contribution of antecedents to the outcome (Albahri et al., 2021, 2022; Alnoor et al., 2024).”
Blockchain adoption in logistics companies in Ho Chi Minh City, Vietnam
Nguyen, L.-T., Nguyen, D.-T., Ngoc, K. N. N., & Đức, Đ. T. V. (2023) · Cogent Business & Management · doi:10.1080/23311975.2023.2216436 · Cited by 47 · see citing works
A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology
Albahri, A., Al-qaysi, Z., Alzubaidi, L., Alnoor, A., Albahri, O., Alamoodi, A., & Bakar, A. A. (2023) · International Journal of Telemedicine and Applications · doi:10.1155/2023/7741735 · Cited by 41 · see citing works
“SSVEP implementation for BCI use cases is aimed at directly detecting brain activity and communicating messages to the outside world [8].”
Background
Navigating the power of blockchain strategy: Analysis of technology-organization-environment (TOE) framework and innovation resistance theory using PLS-SEM and ANN insights
Alnoor, A., Abbas, S., Sadaa, A. M., Chew, X., & Bayram, G. E. (2025) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2025.124044 · Cited by 39 · see citing works
Impact of digital technologies on manufacturing firm resilience during COVID-19 pandemic: a PLS-SEM and artificial neural network analysis
Basit, A., Wang, L., Javed, A., Shoaib, M., & Aslam, M. U. (2024) · Journal of Manufacturing Technology Management · doi:10.1108/jmtm-08-2024-0421 · Cited by 38 · see citing works
A Trustworthy and Explainable Framework for Benchmarking Hybrid Deep Learning Models Based on Chest X-Ray Analysis in CAD Systems
Albahri, A. S., Jassim, M. M., Alzubaidi, L., Hamid, R. A., Ahmed, M. A., Al-Qaysi, Z. T., Albahri, O. S., Alamoodi, A. H., Alqaysi, M. E., Mohammed, T. J., et al. (2024) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622024500019 · Cited by 34 · see citing works
Based on the perception of ethics in social commerce platforms: Adopting SEM and MCDM approaches for benchmarking customers in rural communities
Sadaa, A. M., Ganesan, Y., Khaw, K. W., Alnoor, A., Abbas, S., Chew, X., & Bayram, G. E. (2022) · Current Psychology · doi:10.1007/s12144-022-04069-9 · Cited by 32 · see citing works
“…introduced Electronic Commerce Information System and Data Controller Registry System to provide relevant information on e-commerce initiatives and to comply with the consumers’ data protection rules respectively (Al-Abrrow et al., 2021; Albahri et al., 2021a; Alnoor, 2020; Fadhil et al., 2021).”
BackgroundMethod
Attaining sustainable business performance via eco-innovation under ecological regulatory stringency and market turbulence
Larbi-Siaw, O., Hu, X.-H., & Donkor, D. O. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.136404 · Cited by 29 · see citing works
Benchmarking electric power companies’ sustainability and circular economy behaviors: using a hybrid PLS-SEM and MCDM approach
Khaw, K. W., Camilleri, M. A., Tiberius, V., Alnoor, A., & Zaidan, A. S. (2023) · Environment Development and Sustainability · doi:10.1007/s10668-023-02975-x · Cited by 32 · see citing works
“Previous literature recommended using three experts from the field of specialization to perform pairwise comparisons (Albahri et al., 2021a).”
Optimal Operation of Cogeneration Power Plant Integrated with Solar Photovoltaics Using DLS‐WMA and ANN
Reddy, B. K., Giri, N. C., Yemula, P. K., Agyekum, E. B., & Arya, Y. (2024) · International Journal of Energy Research · doi:10.1155/2024/5562804 · Cited by 31 · see citing works
Tourism Destination Performance and Competitiveness: The Impact on Revenues, Jobs, the Economy, and Growth
Purwono, R., Esquivias, M. A., Sugiharti, L., & Rojas, O. (2024) · Journal of Tourism and Services · doi:10.29036/jots.v15i28.629 · Cited by 29 · see citing works
Determinants of cloud computing deployment in South African construction organisations using structural equation modelling and machine learning technique
Aghimien, D. O., Aigbavboa, C. O., Chan, D. W. M., & Aghimien, E. I. (2022) · Engineering Construction & Architectural Management · doi:10.1108/ecam-05-2022-0464 · Cited by 28 · see citing works
Why is Ghana losing the war against illegal gold mining (Galamsey)? An artificial neural network-based investigations
Ampaw, E. M., Chai, J., Jiang, Y., Dumor, K., & Amouzou, E. K. (2023) · Environmental Science and Pollution Research · doi:10.1007/s11356-023-27265-x · Cited by 27 · see citing works
“Through this process, the best estimation relative to the output is obtained (Aryadoust and Baghaei 2016; Albahri et al. 2022).”
BackgroundInfluential
Decoding workplace dynamics: unveiling perceptual and attitudinal drivers of counterproductive work behaviour using hybridSEMANN approach
Abdullah, H. O., & Al‐Abrrow, H. (2024) · International journal of organizational analysis · doi:10.1108/ijoa-10-2023-4019 · Cited by 24 · see citing works
Exploring User Experience in Virtual Industrial Heritage Platforms: Impact of Cultural Identity, Functional Clarity, Scene Interactivity, and Narrative Quality
Zhou, Q., Wang, S., & Wang, J. (2025) · Buildings · doi:10.3390/buildings15020253 · Cited by 24 · see citing works
Modelling and optimization of membrane process for removal of biologics (pathogens) from water and wastewater: Current perspectives and challenges
Sadare, O. O., Oke, D., Olawuni, O. A., Idris, A. O., & Moothi, K. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e29864 · Cited by 22 · see citing works
“These integrated methodologies capitalize on the respective advantages of each modelling tool to address constraints and improve the accuracy of predictions [112].”
Method
Artificial Neural Network and Structural Equation Modeling in the Future
Ferasso, M., & Alnoor, A. (2022) · Springer eBooks · doi:10.1007/978-981-19-6509-8_18 · Cited by 22 · see citing works
Review of artificial neural networks-contribution methods integrated with structural equation modeling and multi-criteria decision analysis for selection customization
Zaidan, A. A., Alnoor, A., Albahri, O. S., Mohammed, R. T., Alamoodi, A. H., Albahri, A. S., Zaidan, B. B., Garfan, S., Hameed, H., Al-Samarraay, M. S., et al. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106643 · Cited by 22 · see citing works
Algorithmic Burnout and Digital Well-Being: Modelling Young Adults’ Resistance to Personalized Digital Persuasion
Balaskas, S., Konstantakopoulou, M., Yfantidou, I., & Komis, K. (2025) · Societies · doi:10.3390/soc15080232 · Cited by 21 · see citing works
Predicting Determinants of Use Mobile Commerce through Modelling Non-Linear Relationships
Abdullah, H. O., Thajil, K. M., Alnoor, A., Al‐Abrrow, H., Khaw, K. W., Chew, X., & Sadaa, A. M. (2022) · Central European Business Review · doi:10.18267/j.cebr.306 · Cited by 22 · see citing works
“At the same time, ANN implies the opposite as ANN can handle non-linear relationships while it cannot test hypotheses (Albahri et al., 2021; Tan et al., 2014; Hew et al., 2016).”
Background
Factors influencing young adults’ organic food purchase intention on fresh food e-commerce platforms
Qi, X., Lv, X., Li, Z., Yang, C., Li, H., & Ploeger, A. (2024) · British Food Journal · doi:10.1108/bfj-04-2024-0417 · Cited by 22 · see citing works
Building trust for sustained generative AI travel adoption
Foroughi, B., Vu, H. T. M., & Thaichon, P. (2025) · Tourism Review · doi:10.1108/tr-05-2025-0554 · Cited by 24 · see citing works
Ethical Impacts, Risks and Challenges of Artificial Intelligence Technologies in Business Consulting: A New Modelling Approach Based on Structural Equations
Gînguță, A., Ştefea, P., Noja, G. G., & Munteanu, V. P. (2023) · Electronics · doi:10.3390/electronics12061462 · Cited by 30 · see citing works
A Hybrid SEM-ANN Approach for Predicting the Impact of Psychological Needs on Satisfaction with Generative AI Use
Arpacı, İ., & Kuşci, İ. (2025) · Technology Knowledge and Learning · doi:10.1007/s10758-025-09817-x · Cited by 23 · see citing works
“…it provides further validation of the findings of SEM; secondly, it captures both the non-linear and linear relationships between independent and dependent variables and thereby provides a more sensitive and accurate measure of the relative strength of each predictor (Albahri et al., 2022).”
The impact of generative AI shopping assistants on E-commerce consumer motivation and behavior: Consumer-AI interaction design
Xie, G. (2025) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2025.102983 · Cited by 26 · see citing works
Sensor-Based Locomotion Data Mining for Supporting the Diagnosis of Neurodegenerative Disorders: A Survey
Zolfaghari, S., Suravee, S., Riboni, D., & Yordanova, K. (2023) · ACM Computing Surveys · doi:10.1145/3603495 · Cited by 17 · see citing works
“Recently bibliometric measurements have been increasingly expanding across various ields [2, 3, 55].”
Background
Digital paradigm shift: Unraveling students’ intentions to embrace Tablet-based Learning through an extended UTAUT2 model
Arthur, F., Arkorful, V., Salifu, I., & Nortey, S. A. (2023) · Cogent Social Sciences · doi:10.1080/23311886.2023.2277340 · Cited by 17 · see citing works
“Recent studies such as those by Albahri et al. (2022), Hair et al. (2020), and Owolabi et al. (2020), have shown that SEM can determine relationships among unobserved constructs, including independent, mediator, moderator, control, and dependent variables.”
Background
Predicting the determinants of consumer’s intention to boycott surrogate Israeli products – evidence on nonlinear relationships from Morocco
Zejjari, I., & Benhayoun, I. (2025) · Journal of Islamic marketing · doi:10.1108/jima-02-2024-0096 · Cited by 20 · see citing works
Deep Learning Approaches to Natural Language Processing for Digital Twins of Patients in Psychiatry and Neurological Rehabilitation
Mikołajewska, E., & Masiak, J. (2025) · Electronics · doi:10.3390/electronics14102024 · Cited by 16 · see citing works
Factors influencing intention to adopt an AI chatbot for learning in higher education: An integrated PLS-SEM, IPMA, and ANN approach
Guo, H., & Erdenebold, T. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100477 · Cited by 23 · see citing works
Outcomes of Industry–University Collaboration in Open Innovation: An Exploratory Investigation of Their Antecedents’ Impact Based on a PLS-SEM and Soft Computing Approach
Băban, C. F., Băban, M., & Rangone, A. (2022) · Mathematics · doi:10.3390/math10060931 · Cited by 16 · see citing works
Utilizing AI algorithms to model and optimize the composite of nanocellulose and hydrogels via a new technique
Shen, B., Qian, B., & Tu, N. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.138903 · Cited by 15 · see citing works
Spurring sustainability commitment strategy of family-owned SMEs: A multi-analytical SEM & ANFIS perspective
Khaw, K. W., Sadaa, A. M., Alnoor, A., Zaidan, A. S., Ganesan, Y., & Chew, X. (2023) · The Journal of High Technology Management Research · doi:10.1016/j.hitech.2023.100453 · Cited by 15 · see citing works
Diminishing returns or inverted U? The curvilinear relationship between eco-innovation and firms' sustainable business performance: the impact of market turbulence
Xuhua, H., Larbi-Siaw, O., & Thompson, E. (2023) · Kybernetes · doi:10.1108/k-01-2023-0003 · Cited by 14 · see citing works
A systematic literature review: Real-time 3D reconstruction method for telepresence system
Fadzli, F. E., Ismail, A. W., & Ishigaki, S. A. K. (2023) · PLoS ONE · doi:10.1371/journal.pone.0287155 · Cited by 14 · see citing works
Voice-based personal assistant (VPA) trust: Investigating competence and integrity
Huang, R., Kim, M., & Lennon, S. J. (2024) · Telematics and Informatics Reports · doi:10.1016/j.teler.2024.100140 · Cited by 16 · see citing works
RETRACTED: Determination of factors affecting customer satisfaction towards “maynilad” water utility company: A structural equation modeling-deep learning neural network hybrid approach
Ong, A. K. S., Prasetyo, Y. T., Sacro, M. C. C., Artes, A. L., Canonoy, M. P. M., Onda, G. K. D., Persada, S. F., Nadlifatin, R., & Robas, K. P. E. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e13798 · Cited by 14 · see citing works
“[68] showed the articles which have currently integrated both SEM with the neural networks since SEM was deemed to be insufficient for analyzing large frameworks with different nonlinear relationships.”
Background
Retracted
Gauging Public Acceptance of Conditionally Automated Vehicles in the United States
Saravanos, A., Pissadaki, E. K., Singh, W. S., & Delfino, D. (2024) · Smart Cities · doi:10.3390/smartcities7020038 · Cited by 13 · see citing works
Data augmentation based estimation for the censored quantile regression neural network model
Hao, R., Weng, C., Liu, X., & Yang, X. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119097 · Cited by 13 · see citing works
Exploring the future of travel: Unveiling tourist behavior and perceptions of metaverse travel through hybrid PLS-SEM and ANN analysis
Wang, G., Jafar, R. M. S., Hussain, S., Jabeen, M., & Al‐Adwan, A. S. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.106648 · Cited by 16 · see citing works
Integrated ANN-Bayes-based travel time prediction modeling for signalized corridors with probe data acquisition paradigm
Lin, W., Wei, H., & Nian, D. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118319 · Cited by 12 · see citing works
An empirical study on driving blockchain adoption in Maritime freight: an Asian business perspective
Singh, S., Pratap, S., Dwivedi, A., & Lakshay, L. (2024) · Journal of Business and Industrial Marketing · doi:10.1108/jbim-11-2023-0691 · Cited by 11 · see citing works
Effect of innovation practices of banks on customer loyalty: an SEM-ANN approach
Tiwari, P. (2023) · Benchmarking An International Journal · doi:10.1108/bij-06-2022-0392 · Cited by 15 · see citing works
Crisis, Resilience and Recovery in Tourism and Hospitality: A Synopsis
Ahmed, M. G., Sadaa, A. M., Alshamry, H. M., Alharbi, M. A., Alnoor, A., & Kareem, A. A. (2022) · Springer eBooks · doi:10.1007/978-981-19-5763-5_1 · Cited by 11 · see citing works
Predicting Adoption Intention of ChatGPT- A Study on Business Professionals of BangladeshPreprint
Emon, M. M. H. (2023) · Research Square · doi:10.21203/rs.3.rs-3749611/v1 · Cited by 12 · see citing works
A two-staged SEM: artificial neural network approach for understanding and predicting the factors of students’ satisfaction with emergency remote teaching
Sangwan, A., Sangwan, A., Sangwan, A., & Punia, P. (2024) · Educational Technology Research and Development · doi:10.1007/s11423-023-10335-9 · Cited by 10 · see citing works
“Partial Least Squares Structural Equation Modeling (PLS-SEM) is rapidly emerging as a prominent method in social research, and many authors have found it to be more appropriate for tackling complex problems that CB-SEM cannot (Albahri et al., 2022).”
A Dynamic Evaluation Method for the Development of Intelligent Construction Technology in the Construction Field Based on Structural Equation Model–System Dynamics Model
Wang, Y., Zhao, J. S., Gao, N., & Shen, F. (2024) · Buildings · doi:10.3390/buildings14020417 · Cited by 10 · see citing works
“Albahri [22], utilizing a two-stage analysis combining Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN), innovatively introduced a novel approach to linear and non-compensatory relationships.”
Method
Blockchain Adoption Strategy from the Perspective of Organization, Technology and Environment Theory: A Hybrid SEM and ANN Approach
Alnoor, A., Atiyah, A. G., & Abbas, S. (2024) · Global Business Review · doi:10.1177/09721509241255482 · Cited by 4 · see citing works
Exploring radio frequency identification tools for sustainable construction projects: a hybrid structural equation modeling and deep neural network approaches
Kineber, A. F., Oke, A. E., Elshaboury, N., Elseknidy, M., Alhusban, M., Zamil, A. M. A., & Altuwaim, A. A. (2024) · Cogent Engineering · doi:10.1080/23311916.2024.2402052 · Cited by 9 · see citing works
“Neural networks are used in this study to prioritize the RFID implementation tools (Albahri et al., 2021; Albahri et al., 2022).”
A new hybrid approach to the impact of renewable energy consumption on economic growth: sectoral differences in European Union countries
Mehedinţu, A., & Șoavă, G. (2024) · Journal of Business Economics and Management · doi:10.3846/jbem.2024.22000 · Cited by 9 · see citing works
“After building SEM models, it used ANN because it provides predictions with better accuracy than SEM (Albahri et al., 2022).”
Method
Exploring social influences and values in promoting sustainable food consumption using hybrid three stage methods
Wu, Y., & Yu, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-89689-4 · Cited by 9 · see citing works
“A 10-fold ANN analysis was carried out to avoid overfitting; 10% of the data were used for testing and 90% were reserved for training 92 .”
Technology protocols and new health regulations for pandemic severity control: an S-O-R theoretical risk reduction approach
Ausaf, A., Yuan, H., & Nasir, S. A. (2023) · Kybernetes · doi:10.1108/k-12-2022-1684 · Cited by 9 · see citing works
Understanding Environmental Factors Influencing Vegetation Cover Downstream of Dams
Almalki, R., Khaki, M., Saco, P., & Rodriguez, J. F. (2024) · International Journal of Environmental Research · doi:10.1007/s41742-024-00675-6 · Cited by 8 · see citing works
“…component analysis (PCA), support vector machines (SVM), artificial neural network (ANN), moderated mediation analysis, and maximum likelihood (ML) are commonly used for multi-variable analysis (Albahri et al. 2022; Nachappa et al. 2020; Sakaa et al. 2022; Yu et al. 2014; Zerouali et al. 2024).”
The role of moral intention and moral obligation in predicting attitudes toward avoiding AIgiarism: A protection motivation theory perspective
Ali, I. M., & Arpacı, İ. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100681 · Cited by 8 · see citing works
Adoption of telemedicine services among older adults in a low-income developing country: an fsQCA, ANN and PLS-SEM approach
Chanda, R. C., Zadeh, A. V., Hanifah, H. M., Nikbin, D., & Ahmed, T. (2025) · Aslib Journal of Information Management · doi:10.1108/ajim-07-2024-0542 · Cited by 8 · see citing works
The Influence of Crisis Management, Risk-Taking, and Innovation in Sustainability Practices: Empirical Evidence From Iraq
Zaidan, A. S., Khaw, K. W., & Alnoor, A. (2022) · Interdisciplinary Journal of Information Knowledge and Management · doi:10.28945/5019 · Cited by 8 · see citing works
Determinants of Retailers’ Trust in Live-Stream Selling on Social Commerce Platforms: Evidence Using PLS-SEM and FsQCA
Alnoor, A., Salah, M., Abbas, S., Muhsen, Y. R., Sadaa, A. M., Chew, X., & Bayram, G. E. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2514729 · Cited by 7 · see citing works
“The new stream of research is based on the fact that the PLS-SEM method does not have the ability to cover non-linear relationships or provide solutions to the phenomenon under study (Albahri et al., 2021).”
Influential
Identifying and assessing the internet of things implementation barriers for sustainable building projects: SEM-ANN approach
Massoud, M., Kineber, A. F., Elshaboury, N., Abunada, Z., Arashpour, M., Al‐Atroush, M. E., Mostafa, S. F., & Alhusban, M. (2025) · KSCE Journal of Civil Engineering · doi:10.1016/j.kscej.2025.100310 · Cited by 6 · see citing works
The role of drivers for sustainable development in the electric vehicle adoption: A two-staged structural equation modelling-artificial neural network technique
Bansal, R., & Ansari, Y. (2025) · Resources Conservation & Recycling Advances · doi:10.1016/j.rcradv.2025.200255 · Cited by 6 · see citing works
An Orchestration Perspective on Open Innovation between Industry–University: Investigating Its Impact on Collaboration Performance
Băban, C. F., & Băban, M. (2022) · Mathematics · doi:10.3390/math10152672 · Cited by 6 · see citing works
How does big data influence smart manufacturing in the presence of preventive maintenance? A multi-analytical investigation
Samadhiya, A., Naz, F., Kumar, A., Garza‐Reyes, J. A., & Luthra, S. (2024) · Journal of Manufacturing Technology Management · doi:10.1108/jmtm-08-2024-0454 · Cited by 5 · see citing works
Identifying factors influencing online learning outcomes for middle-school students — a re-examination based on XGBoost and SHAP
Yang, S., & Zhu, S. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13405-y · Cited by 5 · see citing works
“Furthermore, neither method can fit complex nonlinear relationships between influencing factors (Albahri et al., 2022) or reveal the trend of each factor’s influence on OLO from a specific range of values (Xiao & Hu, 2023).”
The Paradox of Mobile Money Adoption among Ghana’s PETTY Traders: A Two-Stage PLS-SEM-ANN Approach
Dodoo, R. N. A., Xin, F., Mangudhla, T., Boadi, E. B., Dimén, L., & Nuţă, A. C. (2025) · SAGE Open · doi:10.1177/21582440251378112 · Cited by 5 · see citing works
“PLS-SEM is effective for identifying linear relationships among constructs, but its variance-based approach has limitations, particularly in capturing nonlinear interactions between variables (Albahri et al., 2022).”
Decoding electric vehicle adoption intention in densely populated cities: An integrated hybrid SEM–ANN approach
Chang, R., Chanda, R. C., Zadeh, A. V., & Hanifah, H. M. (2025) · Energy Strategy Reviews · doi:10.1016/j.esr.2025.101964 · Cited by 7 · see citing works
Sri Lankan SMEs’ Performance Through Cloud Computing Adoption: An SEM-ANN Analysis
Nawaz, S. S., & Thelijjagoda, S. (2025) · IEEE Access · doi:10.1109/access.2025.3564339 · Cited by 8 · see citing works
“This factor has been proven to strongly predict innovation adoption [72].”
SEP and Blockchain Adoption in Western Balkans and EU: The Mediating Role of ESG Activities and DEI Initiatives
Basdekidou, V., & Papapanagos, H. (2025) · FinTech · doi:10.3390/fintech4030037 · Cited by 5 · see citing works
“Entrepreneurship in Western Balkans developing countries, as recent democracies in a poor region of Europe with many corruption problems, face many challenges with financing, restrictions on international markets and loans, lack of trust, corruption, and bureaucracy [121–123].”
Influence of Organizational Contingencies on Financial Performance: Mediating Role of Crisis Management
Zaidan, A. S., Khaw, K. W., Chew, X., Alnoor, A., Ganesan, Y., & Sadaa, A. M. (2022) · Central European Business Review · doi:10.18267/j.cebr.320 · Cited by 5 · see citing works
Modeling NFT pricing through aesthetic features: an explainable SEM-neural network framework
Guan, C., Lim, T., & Ding, D. (2025) · Journal of Enterprise Information Management · doi:10.1108/jeim-05-2025-0417 · Cited by 4 · see citing works
Gastro-nostalgia and cultural familiarity shaping tourists’ perceptions of food attributes and future behaviors in culinary tourism
Sharma, I., Aggarwal, A., & Lim, W. M. (2026) · Journal of Hospitality and Tourism Management · doi:10.1016/j.jhtm.2026.101412 · Cited by 4 · see citing works
Recent Techniques in Determining the Effects of Climate Change on Depressive Patients: A Systematic Review
Kader, N. I. A., Yusof, U. K., Khalid, M. N. A., & Husain, N. R. N. (2022) · Journal of Environmental and Public Health · doi:10.1155/2022/1803401 · Cited by 4 · see citing works
Does natural resources efficiency provide roadmap for economic development in China? Evidence from econometric analysis
Dong, Z., & Shi, H. (2023) · Resources Policy · doi:10.1016/j.resourpol.2023.104127 · Cited by 6 · see citing works
The effects of natural resources and integration on green economic recovery: Foreign direct investment and environmentally friendly technologies in China
Lei, Z., Hao, J., Lv, M., & Wei, F. (2023) · Resources Policy · doi:10.1016/j.resourpol.2023.104290 · Cited by 5 · see citing works
PLS-SEM-ANN- Based Technique for Understanding Behavioral Intention to Use a Learning Management System
Pitogo, V. A., Gonzales, E. C., & Jaictin, J. F. P. (2022) · International Conference of Science and Information Technology in Smart Administration (ICSINTESA) · doi:10.1109/icsintesa56431.2022.10041597 · Cited by 4 · see citing works
“The theoretical literature on the use of LMS utilizing two-step analysis is thin [41].”
Background
Creating Value via Transparency and Traceability in Vietnam’s Food Supply Chains: Examining Consumer Trust Using the Dual-stage SEM-ANN Analysis
Nguyen, D. H., & Minh, N. H. (2025) · Journal of Creating Value · doi:10.1177/23949643251350752 · Cited by 3 · see citing works
“ANN also offsets cross-sectional design constraints by quantifying each factor’s contribution (Albahri et al., 2022).”
Explainability, Safety Cues, and Trust in GenAI Advisors: A SEM–ANN Hybrid Study
Balaskas, S., Stamatiou, I., & Androulakis, G. (2025) · Future Internet · doi:10.3390/fi17120566 · Cited by 3 · see citing works
“The number of hidden neurons was set to a small intermediate size (between the number of inputs and the output), following common rules-of-thumb and a brief trial-and-error process to balance prediction accuracy and overfitting risk [94–96].”
Influential
From seeds to standards: how IFRS cultivates ISSB adoption in emerging economies – an SEM-ANN analysis through an institutional perspective in Morocco
Benhayoun, I. (2026) · Meditari Accountancy Research · doi:10.1108/medar-10-2024-2694 · Cited by 4 · see citing works
Belief of Customers in Social Commerce Performed via Social Networking Sites: An Empirical Study from Hồ Chí Minh City
Phan, C. T. T., Đặng, Q. T., & Nguyễn, T. L. (2023) · Vietnam Academy of Social Sciences s VSSR · doi:10.56794/vssr.2(214).61-80 · Cited by 3 · see citing works
“This approach allows for a precise assessment of the relative strength of each predictor and can capture dynamic, nonlinear interactions between independent and dependent variables (Albahri et al., 2022).”
A Hybrid Approach for Predicting Probability of Default in Peer-to-Peer (P2P) Lending Platforms Using Mixture-of-Experts Neural Network
Makokha, C. W., Kube, A. O., & Ngesa, O. (2024) · Journal of Data Analysis and Information Processing · doi:10.4236/jdaip.2024.122009 · Cited by 2 · see citing works
“● Computational Resources: They may require more computational resources and data for training due to their complexity [8].”
BackgroundMethodInfluential
Artificial neural networks in separation processes: Bibliometric review and predictive modeling of drying kinetics
Shing, L. R., Aranha, A. C. R., Matias, G. D. S., Jorge, L. M. D. M., Sípoli, C. C., Suzuki, R. M., Gomes, M. C. S., Sgorlon, J. G., & Defendi, R. O. (2025) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2025.11.003 · Cited by 2 · see citing works
Comfort first: Understanding consumer continuance intention in contactless logistics delivery through human–computer interaction
Yao, X., Su, M., Cai, L., & Qi, G. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104643 · Cited by 2 · see citing works
Enhancing consumer experience in food delivery through hybrid analytical models
Hoyos-Vallejo, C. A., & Carrión-Bósquez, N. (2025) · British Food Journal · doi:10.1108/bfj-07-2025-0939 · Cited by 2 · see citing works
Investigating the factors raising customers concern to continue using AI-supported customer care services of banks: a hybrid SEM-ANN approach
Chanda, R. C., Zadeh, A. V., Hanifah, H. M., Nikbin, D., & Ashrafi, D. M. (2026) · Aslib Journal of Information Management · doi:10.1108/ajim-03-2025-0139 · Cited by 3 · see citing works
Navigating the power of the enterprise metaverse and triple helix model for eco-innovation: evidence from linear and nonlinear analyses
Alnoor, A., Abbas, S., Alazzawi, M. S., Sadaa, A. M., & Chew, X. (2026) · Asia-Pacific Journal of Business Administration · doi:10.1108/apjba-08-2025-0569 · Cited by 2 · see citing works
Revealing the Co-Creation Mechanism of Tourists Supporting the Sustainable Development of Rural Art Tourism Through a Hybrid Model of PLS-SEM and ANN
Zhao, B., Cui, S., & Cheng, X. (2026) · Sustainability · doi:10.3390/su18115230 · Cited by 2 · see citing works
Green Practices in Marketing
Abdulkareem, A. M., & Chakrawal, A. (2022) · Springer eBooks · doi:10.1007/978-981-19-6509-8_15 · Cited by 2 · see citing works

Artificial Neural Network and Structural Equation Modeling Techniques
Zaidan, A. S., & Arianpoor, A. (2022) · Springer eBooks · doi:10.1007/978-981-19-6509-8_1 · Cited by 3 · see citing works
Based on the multi-assessment model: Towards a new context of combining the artificial neural network and structural equation modelling: A review
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., Alamoodi, A. H., et al. (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.111445 · Cited by 87 · see citing works
Artificial Neural Network and Structural Equation Modeling in the Future
Ferasso, M., & Alnoor, A. (2022) · Springer eBooks · doi:10.1007/978-981-19-6509-8_18 · Cited by 22 · see citing works
Artificial Neural Networks and Structural Equation Modeling
Alnoor, A., Wah, K. K., & Hassan, A. (2022) · Springer eBooks · doi:10.1007/978-981-19-6509-8 · Cited by 10 · see citing works
Review of artificial neural networks-contribution methods integrated with structural equation modeling and multi-criteria decision analysis for selection customization
Zaidan, A. A., Alnoor, A., Albahri, O. S., Mohammed, R. T., Alamoodi, A. H., Albahri, A. S., Zaidan, B. B., Garfan, S., Hameed, H., Al-Samarraay, M. S., et al. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106643 · Cited by 22 · see citing works

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: Albahri, A. S.
Alnoor, Alhamzah
Zaidan, A. A.
Albahri, O. S.
Zaidan, B. B.
Peh, S. S.
Masnan, A. H. B.
Yass, A. A.
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: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
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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Hybrid artificial neural network and structural equation modelling techniques: A survey

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