How to Improve Customer Engagement in Social Networks: A Study of Spanish Brands in the Automotive Industry
The objective of this research is to identify to what extent volumes, components, time slots, and publication topics improve customer engagement with Spanish automotive brands through social networks. The study considers thirteen brands and the total number of publications created by them in 2020 (2...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Journal of Theoretical and Applied Electronic Commerce Research |
| Subjects: | |
| Online Access: | https://www.mdpi.com/0718-1876/16/7/177 |
| _version_ | 1797503070739890176 |
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| author | Luis Matosas-López Alberto Romero-Ania |
| author_facet | Luis Matosas-López Alberto Romero-Ania |
| author_sort | Luis Matosas-López |
| collection | DOAJ |
| description | The objective of this research is to identify to what extent volumes, components, time slots, and publication topics improve customer engagement with Spanish automotive brands through social networks. The study considers thirteen brands and the total number of publications created by them in 2020 (23,670 publications) on the social network Twitter. Applying machine learning algorithms followed by multiple linear regression techniques, the authors examine how the variables previously mentioned affect a customer engagement indicator developed for this purpose. The results reveal that while publication components (links, mentions, and hashtags) and the publication time slot do not affect customer engagement, the volume of retweets made by the brand and publications on customer experience topics (without a direct commercial purpose) significantly improve the customer engagement indicator. The authors conclude that customer engagement in social networks can only be improved by conducting exhaustive analyses of activity data for these platforms. However, such analyses must not be done via generic multisector analyses, which only generate superficial and inapplicable knowledge, but rather through detailed studies for each sector. |
| first_indexed | 2024-03-10T03:45:15Z |
| format | Article |
| id | doaj.art-bd9eb7e281214883b6cdd13ee0de1eca |
| institution | Directory Open Access Journal |
| issn | 0718-1876 |
| language | English |
| last_indexed | 2024-03-10T03:45:15Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Theoretical and Applied Electronic Commerce Research |
| spelling | doaj.art-bd9eb7e281214883b6cdd13ee0de1eca2023-11-23T09:09:45ZengMDPI AGJournal of Theoretical and Applied Electronic Commerce Research0718-18762021-11-011673269328110.3390/jtaer16070177How to Improve Customer Engagement in Social Networks: A Study of Spanish Brands in the Automotive IndustryLuis Matosas-López0Alberto Romero-Ania1Department of Financial Economics and Accounting, Rey Juan Carlos University, 28933 Madrid, SpainDepartment of Applied Economics, Rey Juan Carlos University, 28933 Madrid, SpainThe objective of this research is to identify to what extent volumes, components, time slots, and publication topics improve customer engagement with Spanish automotive brands through social networks. The study considers thirteen brands and the total number of publications created by them in 2020 (23,670 publications) on the social network Twitter. Applying machine learning algorithms followed by multiple linear regression techniques, the authors examine how the variables previously mentioned affect a customer engagement indicator developed for this purpose. The results reveal that while publication components (links, mentions, and hashtags) and the publication time slot do not affect customer engagement, the volume of retweets made by the brand and publications on customer experience topics (without a direct commercial purpose) significantly improve the customer engagement indicator. The authors conclude that customer engagement in social networks can only be improved by conducting exhaustive analyses of activity data for these platforms. However, such analyses must not be done via generic multisector analyses, which only generate superficial and inapplicable knowledge, but rather through detailed studies for each sector.https://www.mdpi.com/0718-1876/16/7/177customer engagementsocial networksTwitterautomotive industrymachine learning algorithmsmultiple linear regression |
| spellingShingle | Luis Matosas-López Alberto Romero-Ania How to Improve Customer Engagement in Social Networks: A Study of Spanish Brands in the Automotive Industry Journal of Theoretical and Applied Electronic Commerce Research customer engagement social networks automotive industry machine learning algorithms multiple linear regression |
| title | How to Improve Customer Engagement in Social Networks: A Study of Spanish Brands in the Automotive Industry |
| title_full | How to Improve Customer Engagement in Social Networks: A Study of Spanish Brands in the Automotive Industry |
| title_fullStr | How to Improve Customer Engagement in Social Networks: A Study of Spanish Brands in the Automotive Industry |
| title_full_unstemmed | How to Improve Customer Engagement in Social Networks: A Study of Spanish Brands in the Automotive Industry |
| title_short | How to Improve Customer Engagement in Social Networks: A Study of Spanish Brands in the Automotive Industry |
| title_sort | how to improve customer engagement in social networks a study of spanish brands in the automotive industry |
| topic | customer engagement social networks automotive industry machine learning algorithms multiple linear regression |
| url | https://www.mdpi.com/0718-1876/16/7/177 |
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