Predicting trust in online advertising with an SEM-artificial neural network approach

Leong, Lai-Ying; Hew, Teck-Soon; Ooi, Keng‐Boon; Dwivedi, Yogesh K.

doi:10.1016/j.eswa.2020.113849

Cited by 69 publications

(59 citation statements)

References 118 publications

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“…Thus, the multilayer expectations of a feedforward backpropagation (BP) ANN algorithm have generated normalised importance [ 89 ]. In accordance with [ 18 , 55 , 56 , 62 , 70 ], the benefits of combining SEM with ANN are consumer preference analysis, smartphone repurchase, illustrating the mechanism of trust in online advertising and predicting the motivators that affect cloud computing adoption. In addition, ANN validates SEM results and understands the most important aspects [ 84 ].…”

Section: Discussionmentioning

confidence: 89%

“…Similarly, trust plays a crucial role in online advertising because advertisements are affected by consumer confidence. The literature on this topic contributes to the adoption of SEM–ANN to explore factors that influence trust in online advertising [ 56 ].…”

Section: Search Results and Statistical Information Of Articlesmentioning

confidence: 99%

“…Furthermore, the transversal nature of a data collection method hinders the appropriate assessment of users’ behavioural development. Therefore, the longitudinal approach is appropriate because it can test the strength of relationships [ 7 , 54 – 56 , 58 , 62 , 85 , 98 ].…”

Section: Discussionmentioning

confidence: 99%

“…4.2 , most of the studies focused on the Malaysian context. The results were limited to the Malaysian perspective, and thus, a new study should be conducted in other geographical areas [ 56 , 59 , 63 ]. Most of the studies were performed in Asian countries, and therefore, the results are unsuitable for generalisation; such research should also be conducted in European countries [ 58 , 71 , 85 ].…”

Section: Discussionmentioning

confidence: 99%

“…ANN cannot explain 100% confidence in online advertising because it focuses on specific variables. More theories increase the predictive power of a research model [ 56 ]. Similarly, ANN can predict with an accuracy of 76.4%.…”

Section: Discussionmentioning

confidence: 99%

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

Albahri

Alnoor

Zaidan

et al. 2021

Complex Intell. Syst.

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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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Section: Discussionmentioning

confidence: 89%

Section: Search Results and Statistical Information Of Articlesmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

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Rise of multiattribute decision‐making in combating COVID‐19: A systematic review of the state‐of‐the‐art literature

Alsalem

Mohammed²,

Albahri

et al. 2021

Int J of Intelligent Sys

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Considering the coronavirus disease 2019 (COVID‐19) pandemic, the government and health sectors are incapable of making fast and reliable decisions, particularly given the various effects of decisions on different contexts or countries across multiple sectors. Therefore, leaders often seek decision support approaches to assist them in such scenarios. The most common decision support approach used in this regard is multiattribute decision‐making (MADM). MADM can assist in enforcing the most ideal decision in the best way possible when fed with the appropriate evaluation criteria and aspects. MADM also has been of great aid to practitioners during the COVID‐19 pandemic. Moreover, MADM shows resilience in mitigating consequences in health sectors and other fields. Therefore, this study aims to analyse the rise of MADM techniques in combating COVID‐19 by presenting a systematic literature review of the state‐of‐the‐art COVID‐19 applications. Articles on related topics were searched in four major databases, namely, Web of Science, IEEE Xplore, ScienceDirect, and Scopus, from the beginning of the pandemic in 2019 to April 2021. Articles were selected on the basis of the inclusion and exclusion criteria for the identified systematic review protocol, and a total of 51 articles were obtained after screening and filtering. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature. This taxonomy was drawn on the basis of four major categories, namely, medical ( n = 30), social ( n = 4), economic ( n = 13) and technological ( n = 4). Deep analysis for each category was performed in terms of several aspects, including issues and challenges encountered, contributions, data set, evaluation criteria, MADM techniques, evaluation and validation and bibliography analysis. This study emphasised the current standpoint and opportunities for MADM in the midst of the COVID‐19 pandemic and promoted additional efforts towards understanding and providing new potential future directions to fulfil the needs of this study field.

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Influence of Online Marketing on Gen X’s Loyalty Towards E-Commerce Platform in Malaysia: Case Study of Lazada

Hua

Dada

2023

Contributions to Management Science

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Predicting trust in online advertising with an SEM-artificial neural network approach

Cited by 69 publications

References 118 publications

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

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

Rise of multiattribute decision‐making in combating COVID‐19: A systematic review of the state‐of‐the‐art literature

Influence of Online Marketing on Gen X’s Loyalty Towards E-Commerce Platform in Malaysia: Case Study of Lazada

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