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Record Nr. |
UNINA9910631087503321 |
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Titolo |
Artificial Neural Networks and Structural Equation Modeling : Marketing and Consumer Research Applications / / edited by Alhamzah Alnoor, Khaw Khai Wah, Azizul Hassan |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 |
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ISBN |
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Edizione |
[1st ed. 2022.] |
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Descrizione fisica |
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1 online resource (336 pages) |
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Collana |
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Mathematics and Statistics Series |
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Disciplina |
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Soggetti |
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Marketing |
Consumer behavior |
Neural networks (Computer science) |
Consumer Behavior |
Mathematical Models of Cognitive Processes and Neural Networks |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Chapter 1. Artificial neural network and structural equation modeling techniques -- Chapter 2. Social commerce determinants -- Chapter 3. Technology acceptance model in social commerce -- Chapter 4. Mobile commerce and social commerce -- Chapter 5. Electronic word of mouth and social commerce. |
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Sommario/riassunto |
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This book goes into a detailed investigation of adapting artificial neural network (ANN) and structural equation modeling (SEM) techniques in marketing and consumer research. The aim of using a dual-stage SEM and ANN approach is to obtain linear and non-compensated relationships because the ANN method captures non-compensated relationships based on the black box technology of artificial intelligence. Hence, the ANN approach validates the results of the SEM method. In addition, such the novel emerging approach increases the validity of the prediction by determining the importance of the variables. Consequently, the number of studies using SEM-ANN has increased, but the different types of study cases that show customization of different processes in ANNs method combination with SEM are still unknown, and this aspect will be affecting to the |
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generation results. Thus, there is a need for further investigation in marketing and consumer research. This book bridges the significant gap in this research area. The adoption of SEM and ANN techniques in social commerce and consumer research is massive all over the world. Such an expansion has generated more need to learn how to capture linear and non-compensatory relationships in such area. This book would be a valuable reading companion mainly for business and management students in higher academic organizations, professionals, policy-makers, and planners in the field of marketing. This book would also be appreciated by researchers who are keenly interested in social commerce and consumer research. |
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