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E-Commerce Shopping Motivation and the Influence of Persuasive Strategies

Persuasive strategies are used to influence the behavior or attitude of people without coercion and are commonly used in online systems such as e-commerce systems. However, in order to make persuasive strategies more effective, research suggests that they should be tailored to groups of similar indi...

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Autores principales: Adaji, Ifeoma, Oyibo, Kiemute, Vassileva, Julita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861237/
https://www.ncbi.nlm.nih.gov/pubmed/33733184
http://dx.doi.org/10.3389/frai.2020.00067
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author Adaji, Ifeoma
Oyibo, Kiemute
Vassileva, Julita
author_facet Adaji, Ifeoma
Oyibo, Kiemute
Vassileva, Julita
author_sort Adaji, Ifeoma
collection PubMed
description Persuasive strategies are used to influence the behavior or attitude of people without coercion and are commonly used in online systems such as e-commerce systems. However, in order to make persuasive strategies more effective, research suggests that they should be tailored to groups of similar individuals. Research in the traits that are effective in tailoring or personalizing persuasive strategies is an ongoing research area. In the present study, we propose the use of shoppers' online shopping motivation in tailoring six commonly used influence strategies: scarcity, authority, consensus, liking, reciprocity, and commitment. We aim to identify how these influence strategies can be tailored or personalized to e-commerce shoppers based on the online consumers' motivation when shopping. To achieve this, a research model was developed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) and tested by conducting a study of 226 online shoppers. The result of our structural model suggests that persuasive strategies can influence e-commerce shoppers in various ways depending on the shopping motivation of the shopper. Balanced buyers—the shoppers who typically plan their shopping ahead and are influenced by the desire to search for information online—have the strongest influence on commitment strategy and have insignificant effects on the other strategies. Convenience shoppers—those motivated to shop online because of convenience—have the strongest influence on scarcity, while store-oriented shoppers—those who are motivated by the need for social interaction and immediate possession of goods—have the strongest influence on consensus. Variety seekers—consumers who are motivated to shop online because of the opportunity to search through a variety of products and brands, on the other hand, have the strongest influence on authority.
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spelling pubmed-78612372021-03-16 E-Commerce Shopping Motivation and the Influence of Persuasive Strategies Adaji, Ifeoma Oyibo, Kiemute Vassileva, Julita Front Artif Intell Artificial Intelligence Persuasive strategies are used to influence the behavior or attitude of people without coercion and are commonly used in online systems such as e-commerce systems. However, in order to make persuasive strategies more effective, research suggests that they should be tailored to groups of similar individuals. Research in the traits that are effective in tailoring or personalizing persuasive strategies is an ongoing research area. In the present study, we propose the use of shoppers' online shopping motivation in tailoring six commonly used influence strategies: scarcity, authority, consensus, liking, reciprocity, and commitment. We aim to identify how these influence strategies can be tailored or personalized to e-commerce shoppers based on the online consumers' motivation when shopping. To achieve this, a research model was developed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) and tested by conducting a study of 226 online shoppers. The result of our structural model suggests that persuasive strategies can influence e-commerce shoppers in various ways depending on the shopping motivation of the shopper. Balanced buyers—the shoppers who typically plan their shopping ahead and are influenced by the desire to search for information online—have the strongest influence on commitment strategy and have insignificant effects on the other strategies. Convenience shoppers—those motivated to shop online because of convenience—have the strongest influence on scarcity, while store-oriented shoppers—those who are motivated by the need for social interaction and immediate possession of goods—have the strongest influence on consensus. Variety seekers—consumers who are motivated to shop online because of the opportunity to search through a variety of products and brands, on the other hand, have the strongest influence on authority. Frontiers Media S.A. 2020-11-23 /pmc/articles/PMC7861237/ /pubmed/33733184 http://dx.doi.org/10.3389/frai.2020.00067 Text en Copyright © 2020 Adaji, Oyibo and Vassileva. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Adaji, Ifeoma
Oyibo, Kiemute
Vassileva, Julita
E-Commerce Shopping Motivation and the Influence of Persuasive Strategies
title E-Commerce Shopping Motivation and the Influence of Persuasive Strategies
title_full E-Commerce Shopping Motivation and the Influence of Persuasive Strategies
title_fullStr E-Commerce Shopping Motivation and the Influence of Persuasive Strategies
title_full_unstemmed E-Commerce Shopping Motivation and the Influence of Persuasive Strategies
title_short E-Commerce Shopping Motivation and the Influence of Persuasive Strategies
title_sort e-commerce shopping motivation and the influence of persuasive strategies
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861237/
https://www.ncbi.nlm.nih.gov/pubmed/33733184
http://dx.doi.org/10.3389/frai.2020.00067
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