LEADER 03995nam 22006255 450 001 9910631087503321 005 20251009110000.0 010 $a981-19-6509-9 024 7 $a10.1007/978-981-19-6509-8 035 $a(MiAaPQ)EBC7143448 035 $a(Au-PeEL)EBL7143448 035 $a(CKB)25402360600041 035 $a(PPN)266356273 035 $a(DE-He213)978-981-19-6509-8 035 $a(OCoLC)1492919845 035 $a(EXLCZ)9925402360600041 100 $a20221119d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Neural Networks and Structural Equation Modeling $eMarketing and Consumer Research Applications /$fedited by Alhamzah Alnoor, Khaw Khai Wah, Azizul Hassan 205 $a1st ed. 2022. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2022. 215 $a1 online resource (336 pages) 225 1 $aMathematics and Statistics Series 311 08$aPrint version: Alnoor, Alhamzah Artificial Neural Networks and Structural Equation Modeling Singapore : Springer,c2023 9789811965081 320 $aIncludes bibliographical references. 327 $aChapter 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. 330 $aThis book goes into a detailed investigation of adapting artificial neural network (ANN) and structural equation modeling (SEM) techniques in marketing and consumer research. The aim of using a dual-stage SEM and ANN approach is to obtain linear and non-compensated relationships because the ANN method captures non-compensated relationships based on the black box technology of artificial intelligence. Hence, the ANN approach validates the results of the SEM method. In addition, such the novel emerging approach increases the validity of the prediction by determining the importance of the variables. Consequently, the number of studies using SEM-ANN has increased, but the different types of study cases that show customization of different processes in ANNs method combination with SEM are still unknown, and this aspect will be affecting to the generation results. Thus, there is a need for further investigation in marketing and consumer research. This book bridges the significant gap in this research area. The adoption of SEM and ANN techniques in social commerce and consumer research is massive all over the world. Such an expansion has generated more need to learn how to capture linear and non-compensatory relationships in such area. This book would be a valuable reading companion mainly for business and management students in higher academic organizations, professionals, policy-makers, and planners in the field of marketing. This book would also be appreciated by researchers who are keenly interested in social commerce and consumer research. 410 0$aMathematics and Statistics Series 606 $aMarketing 606 $aConsumer behavior 606 $aNeural networks (Computer science) 606 $aMarketing 606 $aConsumer Behavior 606 $aMathematical Models of Cognitive Processes and Neural Networks 615 0$aMarketing. 615 0$aConsumer behavior. 615 0$aNeural networks (Computer science) 615 14$aMarketing. 615 24$aConsumer Behavior. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 676 $a658.8342 702 $aHassan$b Azizul 702 $aWah$b Khaw Khai 702 $aAlnoor$b Alhamzah 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910631087503321 996 $aArtificial neural networks and structural equation modeling$93083177 997 $aUNINA