Artificial Neural Networks and Structural Equation Modeling
Marketing and Consumer Research Applications
- Publisher's listprice EUR 171.19
-
71 001 Ft (67 620 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 20% (cc. 14 200 Ft off)
- Discounted price 56 801 Ft (54 096 Ft + 5% VAT)
Subcribe now and take benefit of a favourable price.
Subscribe
71 001 Ft
Availability
printed on demand
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Edition number 1st ed. 2022
- Publisher Springer Nature Singapore
- Date of Publication 21 November 2023
- Number of Volumes 1 pieces, Book
- ISBN 9789811965111
- Binding Paperback
- See also 9789811965081
- No. of pages341 pages
- Size 235x155 mm
- Weight 539 g
- Language English
- Illustrations IX, 341 p. 18 illus., 8 illus. in color. Illustrations, black & white 505
Categories
Long description:
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 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.
More
Table of Contents:
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.
