How to Improve Customer Engagement in Social Networks: A Study of Spanish Brands in the Automotive Industry
Abstract
:1. Introduction
1.1. Customer Engagement in Social Networks
1.2. Objectives
2. Materials and Methods
2.1. Sample Design and Data Extraction
2.2. Data Screening and Construction of Variables
2.2.1. Independent Variables
2.2.2. Dependent Variable
2.3. Data Analysis
3. Results
4. Discussion
Answers to the Posed Research Questions
5. Conclusions
5.1. Managerial or Practical Implications
- The study confirms the need for social media professionals to perform detailed analyses on the aspects that influence customer engagement within the sector in which their company operates.
- Variables such as volumes, components, time slots, and, of course, publication topics should be analyzed, but not through generic multisector analyses that lead to merely superficial and inapplicable knowledge but with exhaustive and detailed examinations for each sector.
- Sectorial analyses enable us to extract underlying knowledge from each medium and for each industry. This knowledge may serve as a basis for decision-making processes that improve customer engagement with brands in each sector.
5.2. Limitations and Future Lines of Study
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
- Caci, B.; Cardaci, M.; Tabacchi, M.E. Facebook as a Small World: A topological hypothesis. Soc. Netw. Anal. Min. 2012, 2, 163–167. [Google Scholar] [CrossRef]
- Bhattacharyya, P.; Garg, A.; Wu, S.F. Analysis of user keyword similarity in online social networks. Soc. Netw. Anal. Min. 2011, 1, 143–158. [Google Scholar] [CrossRef]
- Gómez-García, M.; Matosas-López, L.; Palmero-Ruiz, J. Social Networks Use Patterns among University Youth: The Validity and Reliability of an Updated Measurement Instrument. Sustainability 2020, 12, 3503. [Google Scholar] [CrossRef]
- Tarullo, R. ¿Por qué los y las jóvenes están en las redes sociales? Un análisis de sus motivaciones a partir de la teoría de usos y gratificaciones. Prism. Soc. 2020, 29, 222–239. [Google Scholar]
- Alvertis, I.; Biliri, E.; Lampathaki, F.; Askounis, D. Social Agents to Enable Pervasive Social Networking Services. J. Theor. Appl. Electron. Commer. Res. 2018, 13, 50–84. [Google Scholar] [CrossRef] [Green Version]
- Zhu, G.; Liu, H.; Feng, M. Sustainability of Information Security Investment in Online Social Networks: An Evolutionary Game-Theoretic Approach. Mathematics 2018, 6, 177. [Google Scholar] [CrossRef] [Green Version]
- Calvete, E.; Orue, I.; Estévez, A.; Villardón, L.; Padilla, P. Cyberbullying in adolescents: Modalities and aggressors’ profile. Comput. Human Behav. 2010, 26, 1128–1135. [Google Scholar] [CrossRef]
- Masroor, F.; Khan, Q.N.; Aib, I.; Ali, Z. Polarization and Ideological Weaving in Twitter Discourse of Politicians. Soc. Media + Soc. 2019, 5, 1–14. [Google Scholar] [CrossRef]
- Kizgin, H.; Jamal, A.; Rana, N.; Dwivedi, Y.; Weerakkody, V. The impact of social networking sites on socialization and political engagement: Role of acculturation. Technol. Forecast. Soc. Chang. 2019, 145, 503–512. [Google Scholar] [CrossRef]
- Beta, A.R. Commerce, piety and politics: Indonesian young Muslim women’s groups as religious influencers. New Media Soc. 2019, 21, 2140–2159. [Google Scholar] [CrossRef]
- Chen, J.; Chen, H.; Hu, D.; Pan, J.Z.; Zhou, Y. Smog disaster forecasting using social web data and physical sensor data. In Proceedings of the IEEE International Conference on Big Data, IEEE Big Data 2015; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2015; pp. 991–998. [Google Scholar]
- Agur, C.; Frisch, N. Digital Disobedience and the Limits of Persuasion: Social Media Activism in Hong Kong’s 2014 Umbrella Movement. Soc. Media Soc. 2019, 5, 1–12. [Google Scholar] [CrossRef] [Green Version]
- Li, Q.; Zhou, B.; Liu, Q. Can twitter posts predict stock behavior? A study of stock market with twitter social emotion. In Proceedings of the IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA 2016), Chengdu, China, 5–7 July 2016; Institute of Electrical and Electronics Engineers Inc.: Chengdu, China, 2016; pp. 359–364. [Google Scholar]
- Palacios-Marqués, D.; Gallego-Nicholls, J.F.; Guijarro-García, M. A recipe for success: Crowdsourcing, online social networks, and their impact on organizational performance. Technol. Forecast. Soc. Chang. 2021, 165, 120566. [Google Scholar] [CrossRef]
- Yang, X.; Cao, D.; Andrikopoulos, P.; Yang, Z.; Bass, T. Online social networks, media supervision and investment efficiency: An empirical examination of Chinese listed firms. Technol. Forecast. Soc. Chang. 2020, 154, 119969. [Google Scholar] [CrossRef]
- Zerfass, A.; Tench, R.; Verhoeven, P.; Verčič, D.; Moreno, Á. European Communication Monitor 2020; European Public Relations Education and Research Association: Brussels, Belgium, 2020. [Google Scholar]
- Leek, S.; Houghton, D.; Canning, L. Twitter and behavioral engagement in the healthcare sector: An examination of product and service companies. Ind. Mark. Manag. 2019, 81, 115–129. [Google Scholar] [CrossRef]
- Juntunen, M.; Ismagilova, E.; Oikarinen, E.L. B2B brands on Twitter: Engaging users with a varying combination of social media content objectives, strategies, and tactics. Ind. Mark. Manag. 2020, 89, 630–641. [Google Scholar] [CrossRef]
- Richter, D.; Riemer, K.; vom Brocke, J. Internet Social Networking. Bus. Inf. Syst. Eng. 2011, 3, 89–101. [Google Scholar] [CrossRef] [Green Version]
- Vinerean, S.; Opreana, A. Measuring Customer Engagement in Social Media Marketing: A Higher-Order Model. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 16070145. [Google Scholar] [CrossRef]
- Sajjad, M.; Zaman, U. Innovative perspective of marketing engagement: Enhancing users’ loyalty in social media through blogging. J. Open Innov. Technol. Mark. Complex. 2020, 6, 93. [Google Scholar] [CrossRef]
- Schau, H.J.; Muñiz, A.M.; Arnould, E.J. How Brand Community Practices Create Value. J. Mark. 2009, 73, 30–51. [Google Scholar] [CrossRef]
- Chan, K.W.; Li, S.Y. Understanding consumer-to-consumer interactions in virtual communities: The salience of reciprocity. J. Bus. Res. 2010, 63, 1033–1040. [Google Scholar] [CrossRef] [Green Version]
- Jahn, B.; Kunz, W. How to transform consumers into fans of your brand. J. Serv. Manag. 2012, 23, 344–361. [Google Scholar] [CrossRef]
- Habibi, M.R.; Laroche, M.; Richard, M.O. The roles of brand community and community engagement in building brand trust on social media. Comput. Human Behav. 2014, 37, 152–161. [Google Scholar] [CrossRef]
- Pansari, A.; Kumar, V. Customer engagement: The construct, antecedents, and consequences. J. Acad. Mark. Sci. 2017, 45, 294–311. [Google Scholar] [CrossRef]
- Giakoumaki, C.; Krepapa, A. Brand engagement in self-concept and consumer engagement in social media: The role of the source. Psychol. Mark. 2019, 37, 457–465. [Google Scholar] [CrossRef]
- Chen, G.M. Tweet this: A uses and gratifications perspective on how active Twitter use gratifies a need to connect with others. Comput. Human Behav. 2011, 27, 755–762. [Google Scholar] [CrossRef]
- Tafesse, W. Content strategies and audience response on Facebook brand pages. Mark. Intell. Plan. 2015, 33, 927–943. [Google Scholar] [CrossRef]
- Pletikosa Cvijikj, I.; Michahelles, F. Online engagement factors on Facebook brand pages. Soc. Netw. Anal. Min. 2013, 3, 843–861. [Google Scholar] [CrossRef]
- Hutter, K.; Hautz, J.; Dennhardt, S.; Füller, J. The impact of user interactions in social media on brand awareness and purchase intention: The case of MINI on Facebook. J. Prod. Brand Manag. 2013, 22, 342–351. [Google Scholar] [CrossRef] [Green Version]
- Aleti, T.; Harrigan, P.; Cheong, M.; Turner, W. An investigation of how the Australian brewing industry influence consumers on Twitter. Australas. J. Inf. Syst. 2016, 20, 1–20. [Google Scholar] [CrossRef] [Green Version]
- Balan, C. Nike on Instagram: Themes of branded content and their engagement power. In Proceedings of the CBU International Conference, Pragure, Czech Repulic, 17 March 2021; Central Bohemia University: Prague, Czech Republic, 2017; Volume 5, pp. 13–18. [Google Scholar]
- Abdullah, N.S.D.; Zolkepli, I.A. Sentiment analysis of online crowd input towards brand provocation in facebook, twitter, and instagram. In Proceedings of the ACM International Conference, Glasgow, Scotland, 13–17 November 2017; Association for Computing Machinery: New York, NY, USA, 2017; pp. 67–74. [Google Scholar]
- Mariani, M.M.; Mura, M.; Di Felice, M. The determinants of Facebook social engagement for National Tourism Organisations’ Facebook pages: A quantitative approach. J. Destin. Mark. Manag. 2018, 8, 312–325. [Google Scholar] [CrossRef]
- Saura, J.R.; Herraez, B.R.; Reyes-Menendez, A. Comparing a traditional approach for financial brand communication analysis with a big data analytics technique. IEEE Access 2019, 7, 37100–37108. [Google Scholar] [CrossRef]
- Modi, D.; Zhao, L. Social media analysis of consumer opinion on apparel supply chain transparency. J. Fash. Mark. Manag. 2020. [Google Scholar] [CrossRef]
- Lutfeali, S.; Ward, T.; Greene, T.; Arshonsky, J.; Seixas, A.; Dalton, M.; Bragg, M.A. Understanding the extent of adolescents’ willingness to engage with food and beverage companies’ instagram accounts: Experimental survey study. JMIR Public Health Surveill. 2020, 6, e20336. [Google Scholar] [CrossRef] [PubMed]
- Denktaş-Şakar, G.; Sürücü, E. Stakeholder engagement via social media: An analysis of third-party logistics companies. Serv. Ind. J. 2020, 40, 866–889. [Google Scholar] [CrossRef]
- Paliwoda-Matiolanska, A.; Smolak-Lozano, E.; Nakayama, A. Corporate image or social engagement: Twitter discourse on corporate social responsibility (CSR) in public relations strategies in the energy sector. Prof. Inf. 2020, 29, 1–16. [Google Scholar] [CrossRef]
- Cuevas-Molano, E.; Sánchez Cid, M.; Matosas-López, L. Bibliometric analysis of studies of brand content strategy within social media. Comun. Soc. 2019, 2019, e7441. [Google Scholar] [CrossRef]
- Phillips, L.; Dowling, C.; Shaffer, K.; Hodas, N.; Volkova, S. Using Social Media to Predict the Future: A Systematic Literature Review. Comput. Res. Repos. 2017, 2017, 1–55. [Google Scholar]
- Matosas-López, L. Como gestionar de forma eficiente la presencia de empresas del sector del automóvil en twitter. La aplicación de algoritmos de aprendizaje automatizados para la categorización temática de publicaciones. In Proceedings of the Congreso para la Difusión de la Producción Científica e Investigadora, CODIPROCIN 2020, Madrid, Spain, 25 October 2020; Serrano Lozano, J., Ed.; AMEC Ediciones: Madrid, Spain, 2020; p. 22. [Google Scholar]
- Interbrand Best Global Brands 2019; Interbrand: New York, NY, USA, 2019.
- Alkadri, M.F.; Istiani, N.F.F.; Yatmo, Y.A. Mapping Social Media Texts as the Basis of Place-Making Process. Procedia-Soc. Behav. Sci. 2015, 184, 46–55. [Google Scholar] [CrossRef] [Green Version]
- Quintana Pujalte, L.; Sosa Valcarcel, A.; Castillo Esparcia, A. Acciones y estrategias de comunicación en plataformas digitales. El caso Cifuentes. Prism. Soc. 2018, 22, 247–270. [Google Scholar]
- Serrano, E.; Iglesias, C.A. Validating viral marketing strategies in Twitter via agent-based social simulation. Expert Syst. Appl. 2016, 50, 140–150. [Google Scholar] [CrossRef]
- Hanifawati, T.; Ritonga, U.S.; Puspitasari, E.E. Managing brands’ popularity on Facebook: Post time, content, and brand communication strategies. J. Indones. Econ. Bus. 2019, 34, 187–207. [Google Scholar] [CrossRef]
- Hsu, B. Comparison of Supervised Classification Models on Textual Data. Mathematics 2020, 8, 851. [Google Scholar] [CrossRef]
- Simeone, O. A Very Brief Introduction to Machine Learning with Applications to Communication Systems. IEEE Trans. Cogn. Commun. Netw. 2018, 4, 648–664. [Google Scholar] [CrossRef] [Green Version]
- Blachnik, M.; Kordos, M. Comparison of Instance Selection and Construction Methods with Various Classifiers. Appl. Sci. 2020, 10, 3933. [Google Scholar] [CrossRef]
- Wang, Z.; Bai, G.; Chowdhury, S.; Xu, Q.; Seow, Z.L. TwiInsight: Discovering Topics and Sentiments from Social Media Datasets. Comput. Res. Repos. 2017, 2017, 1–14. [Google Scholar]
- Oviedo-García, M.A.; Muñoz-Expósito, M.; Castellanos-Verdugo, M.; Sancho-Mejías, M. Metric proposal for customer engagement in Facebook. J. Res. Interact. Mark. 2014, 8, 327–344. [Google Scholar] [CrossRef]
- Matosas-López, L.; Romero-Ania, A. The Efficiency of Social Network Services Management in Organizations. An In-Depth Analysis Applying Machine Learning Algorithms and Multiple Linear Regressions. Appl. Sci. 2020, 10, 5167. [Google Scholar] [CrossRef]
- Mohd Razali, N.; Bee Wah, Y. Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. J. Stat. Model. Anal. 2011, 2, 21–33. [Google Scholar]
- Gujarati, D.N.; Porter, D.C. Essentials of Econometrics, 4th ed.; McGraw-Hill: New York, NY, USA, 2010. [Google Scholar]
- Matosas López, L. Variables of twitter´s brand activity that influence audience spreading behavior of branded content. Esic Mark. Econ. Bus. J. 2018, 44, 525–546. [Google Scholar] [CrossRef]
- Moro, S.; Rita, P.; Vala, B. Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach. J. Bus. Res. 2016, 69, 3341–3351. [Google Scholar] [CrossRef]
- Soni, A.K. Multi-lingual sentiment analysis of Twitter data by using classification algorithms. In Proceedings of the 2017 2nd IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT 2017, Coimbatore, India, 22–24 February 2017; Institute of Electrical and Electronics Engineers Inc.: Coimbatore, India, 2017. [Google Scholar]
- Jiang, D.; Luo, X.; Xuan, J.; Xu, Z. Sentiment computing for the news event based on the social media big data. IEEE Access 2017, 5, 2373–2382. [Google Scholar] [CrossRef]
- Alonso, M. Las redes sociales como canal de comunicación de las marcas de moda españolas. El caso de Zara, Mango y el Corte Inglés. Index Comun. 2015, 5, 77–105. [Google Scholar]
- Laudano, C.N.; Planas, J.; Kessler, M.I. Aproximaciones a los usos de twitter en bibliotecas universitarias de Argentina. An. Doc. 2016, 19, 1–11. [Google Scholar] [CrossRef] [Green Version]
- Sabate, F.; Berbegal-Mirabent, J.; Cañabate, A.; Lebherz, P.R. Factors influencing popularity of branded content in Facebook fan pages. Eur. Manag. J. 2014, 32, 1001–1011. [Google Scholar] [CrossRef]
- Feng, C.; Fu, L.; Wu, X.; Gan, X.; Wang, X.; Chen, G.; Xu, J. Evolution Matters: Content Transmission in Evolving Wireless Social Networks. IEEE Trans. Wirel. Commun. 2020, 19, 7377–7391. [Google Scholar] [CrossRef]
- Matosas-López, L. The Management of Digital Marketing Strategies in Social Network Services: A Comparison between American and European Organizations. J. Open Innov. Technol. Mark. Complex. 2021, 7, 65. [Google Scholar] [CrossRef]
- Cuevas-Molano, E.; Matosas-López, L.; Bernal-Bravo, C. Factors Increasing Consumer Engagement of Branded Content in Instagram. IEEE Access 2021, 9, 143531–143548. [Google Scholar] [CrossRef]
- Lou, C.; Tan, S.S.; Chen, X. Investigating Consumer Engagement with Influencer- vs. Brand-Promoted Ads: The Roles of Source and Disclosure. J. Interact. Advert. 2019, 19, 169–186. [Google Scholar] [CrossRef]
Author/s | Platform Considered | Industry/Sector | Methodological Approach | Technique Used |
---|---|---|---|---|
Pletikosa Cvijikj and Michahelles [30] | Food | Analysis of activity metrics | Structural equation modeling | |
Hutter et al. [31] | Automotive | Surveys | Structural equation modeling | |
Aleti et al. [32] | Beverage | Analysis of activity metrics | Multiple linear regression | |
Balan [33] | Sport | Analysis of activity metrics | Descriptive analysis | |
Abdullah and Zolkepli [34] | Facebook, Twitter, and Instagram | Restoration | Analysis of activity metrics | Text-Data mining |
Mariani et al. [35] | Tourism | Analysis of activity metrics | Multiple linear regression | |
Saura et al. [36] | Financial | Analysis of activity metrics | Machine learning algorithms | |
Giakoumaki and Krepapa [27] | Luxury | Surveys | Multiple linear regression | |
Modi and Zhao [37] | Twitter and Instagram | Apparel | Analysis of activity metrics | Machine learning algorithms |
Lutfeali et al. [38] | Food and beverage | Surveys | Structural equation modeling | |
Denktaş-Şakar and Sürücü [39] | Logistics | Analysis of activity metrics | Descriptive analysis | |
Paliwoda-Matiolanska et al. [40] | Energy | Analysis of activity metrics | Text-Data mining |
Categories | Num. of Variables | Names of Variables |
---|---|---|
(a) Publication volumes | 4 | Daily Tweets, Original Tweets, Retweets, Replies |
(b) Publication components | 7 | Tweets with Mentions, Tweets with Links, Tweets with Hashtags, Tweets with Mentions and Links, Tweets with Mentions and Hashtags, Tweets with Links and Hashtags, Tweets with Mentions, Links and Hashtags |
(c) Publication time slots | 8 | Pub. from 8:00 to 10:59, Pub. from 11:00 to 13:59, Pub. from 14:00 to 16:59, Pub. from 17:00 to 19:59, Pub. from 20:00 to 22:59, Pub. from 23:00 to 1:59, Pub. from 2:00 to 4:59, Pub. from 5:00 to 7:59 |
(d) Publication topics | 7 | Pub. of each of the seven topics indicated in the following section (Section 2.2.1) |
(e) Observed customer engagement | 1 | Indicator of customer engagement |
Total | 27 |
(e) Observed Customer Engagement | |||
---|---|---|---|
β | t | Sig. p-Value | |
(a) Publication volumes | |||
Daily tweets | 0.033 | 0.214 | 0.835 |
Original tweets | 0.266 | 1.721 | 0.116 |
Retweets | 0.745 | 4.029 | 0.049 * |
Replies | −0.284 | −2.097 | 0.062 |
(b) Publication components | |||
Tweets with Mentions | −0.191 | −1.477 | 0.170 |
Tweets with Links | 0.208 | 1.674 | 0.125 |
Tweets with Hashtags | 0.198 | 1.588 | 0.143 |
Tweets with Mentions and Links | 0.024 | 0.154 | 0.881 |
Tweets with Mentions and Hashtags | 0.017 | 0.112 | 0.913 |
Tweets with Links and Hashtags | 0.201 | 1.607 | 0.139 |
Tweets with Mentions, Links, and Hashtags | 0.072 | 0.482 | 0.64 |
(c) Publication time slots | |||
Pub. from 8:00 to 10:59 | −0.003 | −0.022 | 0.983 |
Pub. from 11:00 to 13:59 | −0.074 | −0.479 | 0.642 |
Pub. from 14:00 to 16:59 | −0.087 | −0.630 | 0.543 |
Pub. from 17:00 to 19:59 | −0.053 | −0.381 | 0.712 |
Pub. from 20:00 to 22:59 | 0.136 | 1.020 | 0.332 |
Pub. from 23:00 to 1:59 | 0.215 | 1.769 | 0.107 |
Pub. from 2:00 to 4:59 | 0.16 | 1.122 | 0.288 |
Pub. from 5:00 to 7:59 | 0.174 | 1.340 | 0.210 |
(d) Publication topics | |||
Pub. Product or brand advertising | −0.153 | −0.360 | 0.726 |
Pub. Promoted events (motor, sport, music, etc.) | −0.015 | −0.042 | 0.968 |
Pub. Launch of new products/services | −0.24 | −0.588 | 0.570 |
Pub. Contests and participatory activities | −0.454 | −1.207 | 0.255 |
Pub. Fairs, forums, and shows | 0.258 | 0.773 | 0.457 |
Pub. Customer experiences (without a direct commercial purpose) | 0.898 | 6.758 | 0.000 ** |
Pub. Technological research, development, and innovation (R + D + I) | −0.357 | −0.734 | 0.480 |
(d) Publication Topics | % of Total Tweets | Average RT Received Per Pub. | Average FV Received Per Pub. |
---|---|---|---|
Pub. Product or brand advertising | 30.00% | 8.14 | 16.71 |
Pub. Promoted events (motor, sport, music, etc.) | 27.93% | 12.19 | 42.30 |
Pub. Launch of new products/services | 13.45% | 7.60 | 17.85 |
Pub. Contests and participatory activities | 12.41% | 11.33 | 28.50 |
Pub. Fairs, forums, and shows | 6.21% | 4.22 | 13.00 |
Pub. Customer experiences (without a direct commercial purpose) | 5.17% | 10.08 | 38.56 |
Pub. Technological research, development, and innovation (R + D + I) | 4.83% | 7.43 | 26.71 |
Total | 100.00% |
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Matosas-López, L.; Romero-Ania, A. How to Improve Customer Engagement in Social Networks: A Study of Spanish Brands in the Automotive Industry. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 3269-3281. https://doi.org/10.3390/jtaer16070177
Matosas-López L, Romero-Ania A. 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. 2021; 16(7):3269-3281. https://doi.org/10.3390/jtaer16070177
Chicago/Turabian StyleMatosas-López, Luis, and Alberto Romero-Ania. 2021. "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 16, no. 7: 3269-3281. https://doi.org/10.3390/jtaer16070177