Social Networks Marketing and Consumer Purchase Behavior: The Combination of SEM and Unsupervised Machine Learning Approaches
The purpose of this paper is to reveal how social network marketing (SNM) can affect consumers’ purchase behavior (CPB). We used the combination of structural equation modeling (SEM) and unsupervised machine learning approaches as an innovative method. The statistical population of the study conclud...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/6/2/35 |
| _version_ | 1797489986417721344 |
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| author | Pejman Ebrahimi Marjan Basirat Ali Yousefi Md. Nekmahmud Abbas Gholampour Maria Fekete-Farkas |
| author_facet | Pejman Ebrahimi Marjan Basirat Ali Yousefi Md. Nekmahmud Abbas Gholampour Maria Fekete-Farkas |
| author_sort | Pejman Ebrahimi |
| collection | DOAJ |
| description | The purpose of this paper is to reveal how social network marketing (SNM) can affect consumers’ purchase behavior (CPB). We used the combination of structural equation modeling (SEM) and unsupervised machine learning approaches as an innovative method. The statistical population of the study concluded users who live in Hungary and use Facebook Marketplace. This research uses the convenience sampling approach to overcome bias. Out of 475 surveys distributed, a total of 466 respondents successfully filled out the entire survey with a response rate of 98.1%. The results showed that all dimensions of social network marketing, such as entertainment, customization, interaction, WoM and trend, had positively and significantly influenced consumer purchase behavior (CPB) in Facebook Marketplace. Furthermore, we used hierarchical clustering and K-means unsupervised algorithms to cluster consumers. The results show that respondents of this research can be clustered in nine different groups based on behavior regarding demographic attributes. It means that distinctive strategies can be used for different clusters. Meanwhile, marketing managers can provide different options, products and services for each group. This study is of high importance in that it has adopted and used plspm and Matrixpls packages in R to show the model predictive power. Meanwhile, we used unsupervised machine learning algorithms to cluster consumer behaviors. |
| first_indexed | 2024-03-10T00:24:31Z |
| format | Article |
| id | doaj.art-2c07fb21c7c24e75ad68cadc354fa9f1 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-10T00:24:31Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-2c07fb21c7c24e75ad68cadc354fa9f12023-11-23T15:35:59ZengMDPI AGBig Data and Cognitive Computing2504-22892022-03-01623510.3390/bdcc6020035Social Networks Marketing and Consumer Purchase Behavior: The Combination of SEM and Unsupervised Machine Learning ApproachesPejman Ebrahimi0Marjan Basirat1Ali Yousefi2Md. Nekmahmud3Abbas Gholampour4Maria Fekete-Farkas5Doctoral School of Economic and Regional Sciences, Hungarian University of Agriculture and Life Sciences (MATE), 2100 Gödöllő, HungaryFaculty of Management, University of Tehran, Tehran 141556311, IranDepartment of Management, Bandar Anzali Branch, Islamic Azad University, Bandar Anzali 4313111111, IranDoctoral School of Economic and Regional Sciences, Hungarian University of Agriculture and Life Sciences (MATE), 2100 Gödöllő, HungaryThe Innovation and Entrepreneurship Research Lab, London EC4N 7TW, UKInstitute of Agricultural and Food Economics, Hungarian University of Agriculture and Life Sciences (MATE), 2100 Gödöllő, HungaryThe purpose of this paper is to reveal how social network marketing (SNM) can affect consumers’ purchase behavior (CPB). We used the combination of structural equation modeling (SEM) and unsupervised machine learning approaches as an innovative method. The statistical population of the study concluded users who live in Hungary and use Facebook Marketplace. This research uses the convenience sampling approach to overcome bias. Out of 475 surveys distributed, a total of 466 respondents successfully filled out the entire survey with a response rate of 98.1%. The results showed that all dimensions of social network marketing, such as entertainment, customization, interaction, WoM and trend, had positively and significantly influenced consumer purchase behavior (CPB) in Facebook Marketplace. Furthermore, we used hierarchical clustering and K-means unsupervised algorithms to cluster consumers. The results show that respondents of this research can be clustered in nine different groups based on behavior regarding demographic attributes. It means that distinctive strategies can be used for different clusters. Meanwhile, marketing managers can provide different options, products and services for each group. This study is of high importance in that it has adopted and used plspm and Matrixpls packages in R to show the model predictive power. Meanwhile, we used unsupervised machine learning algorithms to cluster consumer behaviors.https://www.mdpi.com/2504-2289/6/2/35social networks marketingconsumer purchase behaviorFacebook Marketplacestructural equation modelingmachine learningunsupervised clustering algorithms |
| spellingShingle | Pejman Ebrahimi Marjan Basirat Ali Yousefi Md. Nekmahmud Abbas Gholampour Maria Fekete-Farkas Social Networks Marketing and Consumer Purchase Behavior: The Combination of SEM and Unsupervised Machine Learning Approaches Big Data and Cognitive Computing social networks marketing consumer purchase behavior Facebook Marketplace structural equation modeling machine learning unsupervised clustering algorithms |
| title | Social Networks Marketing and Consumer Purchase Behavior: The Combination of SEM and Unsupervised Machine Learning Approaches |
| title_full | Social Networks Marketing and Consumer Purchase Behavior: The Combination of SEM and Unsupervised Machine Learning Approaches |
| title_fullStr | Social Networks Marketing and Consumer Purchase Behavior: The Combination of SEM and Unsupervised Machine Learning Approaches |
| title_full_unstemmed | Social Networks Marketing and Consumer Purchase Behavior: The Combination of SEM and Unsupervised Machine Learning Approaches |
| title_short | Social Networks Marketing and Consumer Purchase Behavior: The Combination of SEM and Unsupervised Machine Learning Approaches |
| title_sort | social networks marketing and consumer purchase behavior the combination of sem and unsupervised machine learning approaches |
| topic | social networks marketing consumer purchase behavior Facebook Marketplace structural equation modeling machine learning unsupervised clustering algorithms |
| url | https://www.mdpi.com/2504-2289/6/2/35 |
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