An analysis of how Fortune 500 companies respond to users replying to company tweets
- Author(s)
-
DOIhttp://dx.doi.org/10.21511/im.13(4).2017.02
-
Article InfoVolume 13 2017, Issue #4, pp. 17-24
- Cited by
- 961 Views
-
129 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
With hundreds of millions of active users generating almost a half of a billion tweets each day, Twitter has solidified itself as one of the most popular websites in today’s digital world. Because of this popularity, companies seeking to leverage the large audience have gravitated toward Twitter. This study examines how the Fortune 500 uses Twitter by analyzing 9,122 corporate tweets and 1,509 user replies through the use of content analysis. Examined factors include interactivity (non-interactive vs. reactive vs. interactive), company type (B2B vs. B2C), and user reply valence (positive vs. neutral vs. negative). Company response time to user replies is also investigated. The study results point to interactive tweets generating the most engagement. B2Cs not only respond faster to user replies but also generate more engagement than B2Bs. Negative replies can decrease engagement for B2Bs and B2Cs, but the influence on B2Bs is more profound. Companies responded the fastest to negative replies followed by positive replies and neutral replies, respectively. Thus, a company should assess its own business practices, target audience, and ability to perform customer service before creating a social media account such as Twitter.
- Keywords
-
JEL Classification (Paper profile tab)M30, M31, M39
-
References41
-
Tables1
-
Figures1
-
- Fig. 1. Interaction between interactivity and company type on a) number of replies, b) number of retweets, and c) number of likes
-
- Table 1. Summary of multilevel modeling analysis results
-
- Bach, S., & Kim, S. (2012). Online consumer complaint behaviors: The dynamics of service failures, consumers’ word of mouth, and organization-consumer relationships. International Journal of Strategic Communication, 6(1), 59-76.
- Berthon, P., Ewing, M., Pitt, L., & Naudé, P. (2003). Understanding B2B and the Web: The acceleration of coordination and motivation. Industrial Marketing Management, 32(7), 553-561.
- Campbell, C., Papania, L., Parent, M., & Cyr, D. (2010). An exploratory study into brand alignment in B2B relationships. Industrial Marketing Management, 39(5), 712-720.
- Chen, Z., & Lurie, N. (2013). Temporal contiguity and negativity bias in the impact of online word of mouth. Journal of Marketing Research, 50(4), 463-476.
- Eisend, M. (2013). The Moderating influence of involvement on two-sided advertising effects. Psychology & Marketing, 30(7), 566-575.
- Etter, M. (2013). Reasons for low levels of interactivity: (Non-) interactive CSR communication in twitter. Public Relations Review, 39(5), 606-608.
- Himelboim, I., McCreery, S., & Smith, M. (2013). Birds of a feather tweet together: Integrating network and content analyses to examine cross-ideology exposure on Twitter. Journal of Computer-Mediated Communication, 18(2), 154-174.
- Kalman, Y., & Rafaeli, S. (2011). Online pauses and silence: Chronemic expectancy violations in written computer-mediated communication. Communication Research, 38(1), 54-69.
- Kalman, Y., Ravid, G., Raban, D., & Rafaeli, S. (2006). Pauses and response latencies: A chronemic analysis of asynchronous CMC. Journal of Computer-Mediated Communication, 12(1), 1-23.
- Karimova, G. (2011). Can level of interactivity be measured? Empedocles: European Journal for the Philosophy of Communication, 2(2), 291-304.
- Kiousis, S. (2002). Interactivity: A concept explication. New Media & Society, 4(3), 355-383.
- Lee, H., & Park, H. (2013). Testing the impact of message interactivity on relationship management and organizational reputation. Journal of Public Relations Research, 25(2), 188-206.
- Lee, M., Rodgers, S., & Kim, M. (2009). Effects of valence and extremity of eWOM on attitude toward the brand and website. Journal of Current Issues & Research in Advertising, 31(2), 1-11.
- Lee, M., & Youn, S. (2009). Electronic word of mouth (eWOM): How eWOM platforms influence consumer product judgement. International Journal of Advertising, 28(3), 473-499.
- Lohtia, R., Donthu, N., & Hershberger, E. (2003). The impact of content and design elements on banner advertising click-through rates. Journal of Advertising Research, 43(4), 410-418.
- Martin, P. (2008). The mass media as sentinel: Why bad news about issues is good news for participation. Political Communication, 25(2), 180-193.
- Newhagen, J. E., Corders, J. W., & Levy, M. R. (1995). : Audience scope and the perception of interactivity in viewer mail on the Internet. Journal of Communication, 45(3), 164-175.
- Parmar, B. (2015). 50 Companies That Get Twitter – and 50 That Don’t.
- Rafaeli, S. (1988). Interactivity: From new media to communication. In R. Hawkins, J. Weimann, & S. Pingree (Eds.), Advancing communication science: Merging mass and interpersonal processes (pp. 110-134). Newbury Park, CA: Sage.
- Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.).Thousand Oaks, CA: Sage.
- Rapp, A., Beitelspacher, L., Grewal, D., & Hughes, D. (2013). Understanding social media effects across seller, retailer, and consumer interactions. Journal of the Academy of Marketing Science, 41(5), 547-566.
- Reber, B., & Fosdick, S. (2005). Building business relationships online: Relationship management in business-to-business e-commerce. Journal of Website Promotion, 1(1), 13-29.
- Rim, H., & Song, D. (2016). “How negative becomes less negative”: Understanding the effects of comment valence and response sidedness in social media. Journal of Communication.
- Roering, K., & Paul, R. (1976). The effect of the consistency of product claims on the credibility of persuasive messages. Journal of Advertising, 5(2), 32-36.
- Saffer, A., Sommerfeldt, E., & Taylor, M. (2013). The effects of organizational Twitter interactivity on organization–public relationships. Public Relations Review, 39(3), 213-215.
- Schandorf, M. (2013). Mediated gesture: Paralinguistic communication and phatic text. Convergence: The International Journal of Research into New Media Technologies, 19(3), 319-344.
- Scheufele, B., Haas, A., & Brosius, H. (2011). Mirror or molder? A study of media coverage, stock prices, and trading volumes in Germany. Journal of Communication, 61(1), 48-70.
- Schultz, F., Utz, S., & Göritz, A. (2011). Is the medium the message? Perceptions of and reactions to crisis communication via Twitter, blogs and traditional media. Public Relations Review, 37(1), 20-27.
- Song, J., & Zinkhan, G. (2008). Determinants of perceived web site interactivity. Journal of Marketing, 72(2), 99-113.
- Stacks, D. W. (2017). Primer for public relations research. New York: Guilford Press.
- Steyn, P., Salehi-Sangari, E., Pitt, L., Parent, M., & Berthon, P. (2010). The social media release as a public relations tool: Intentions to use among B2B bloggers. Public Relations Review, 36, 87-89.
- Stromer-Galley, J. (2004). Interactivity-as-product and interactivity-as-process. The Information Society, 20(5), 391-394.
- Steuer, J. (1992). Defining virtual reality: Dimensions determining telepresence. Journal of Communication, 42(4), 73-93.
- Sundar, S. (2004). Theorizing interactivity’s effects. The Information Society, 20(5), 385-389.
- Sundar, S. S., Kalyanaraman, S., & Brown, J. (2003). Explicating web site interactivity: Impression formation effects in political campaign sites. Communication Research, 30(1), 30-59.
- Sweetser, K. (2010). A losing strategy: The impact of nondisclosure in social media on relationships. Journal of Public Relations Research, 22(3), 288-312.
- Usunier, J., & Roulin, N. (2010). The influence of high- and low-context communication styles on the design, content, and language of business-to-business web sites. Journal of Business Communication, 47(2), 189-227.
- Waters, R., & Williams, J. (2011). Squawking, tweeting, cooing, and hooting: Analyzing the communication patterns of government agencies on Twitter. Journal of Public Affairs, 11(4), 353-363.
- Westerman, D., Spence, P., & Heide, B. (2014). Social media as information source: Recency of updates and credibility of information. Journal of Computer-Mediated Communication, 19(2), 171-183.
- Wise, K., Hamman, B., & Thorson, K. (2006). Moderation, response rate, and message interactivity: Features of online communities and their effects on intent to participate. Journal of Computer-Mediated Communication, 12(1), 24-41.
- Wu, G. (1999). Perceived interactivity and attitude toward websites. In M. S. Robert (Ed.), Proceedings of the American Academy of Adverting (pp. 254-262). Gainesville, FL.
-
Quantitative study of selected Facebook marketing communication engagement factors in the optics of different post types
Ľudovit Nastišin
,
Richard Fedorko 
,
Vladimir Vavřečka ,
Radovan Bačik ,
Martin Rigelsky
doi: http://dx.doi.org/10.21511/im.15(3).2019.02
The objective of the research was to identify statistically significant differences in selected engagement indicators on Facebook – likes and shares in relation to the different types of content that brands on this platform add to. The analysis was performed on a sample of three global companies from the top 25 most valuable brands in the world and their posts. Using quantitative statistical methods – MANOVA (Multivariate analysis of variance) and Gammes-Howell post hoc test, a total of 1,280 brand posts were analyzed in order to differentiate the liking and sharing of content types. Data collection was carried out in the first half of 2018. The findings pointed to two statistically significant differences that were also interpreted in the discussion of the research. The findings have shown that in case of liking, in two cases out of three, there is a statistically significant difference in terms of the type of content added, when photos came out as those with the greatest potential to get like from Facebook users and fans. At the same time, the same finding appeared in the case of sharing, which is an even stronger form of engagement. Likewise, photos were shown to be the most promising in terms of potential content sharing by Facebook users and fans. The study provided some clues as to where this research should go further and explore the relationship more deeply in view of the more extensive quantitative research, and also the potential qualitative approach. The future research directions include analyzing companies of different types and sizes and also taking into account the contribution from other social networks with the same or similar engagement indicators.
-
Factors affecting users’ brand awareness through social media marketing on TikTok
Cuong Nguyen ,
Thao Tran ,
Tien Nguyen
doi: http://dx.doi.org/10.21511/im.20(1).2024.11
Innovative Marketing Volume 20, 2024 Issue #1 pp. 122-131 Views: 1793 Downloads: 503 TO CITE АНОТАЦІЯTikTok is increasingly influential in promoting brand awareness and boosting purchase intention. From a social media marketing perspective, brand awareness significantly influences consumers’ purchasing decisions. The primary objective of this study is to investigate and measure the factors influencing the brand awareness of TikTok users through social media marketing communications. The paper employs exploratory factor analysis, Cronbach’s Alpha test, and regression analysis to test the hypotheses. 24 observed variables were divided into six groups. Each variable was assessed using a 5-point Likert scale. The sample size includes 240 valid responses from TikTok users collected via convenience sampling. The questionnaire was distributed to TikTok users from Vietnam via the link on Google Forms. The collected data were processed by SPSS 20 software. The results suggest that six independent variables positively affect brand recognition via social media marketing on TikTok, with a 95% confidence interval at Sig. = 0.000 (0.05). Each of the six variables positively impacts initial expectations and is statistically significant at 1%. The results show that trends (Beta = 0.299) are the most decisive factors impacting brand awareness of TikTok users. Besides, electronic word of mouth (Beta = 0.242), influencer (Beta = 0.220), entertainment (Beta = 0.206), interaction (Beta = 0.200), and storytelling (Beta = 0.179) also positively affect the brand awareness of TikTok users. Limitations and further research suggest that marketers should investigate the role of artificial intelligence (AI) in the consumer’s brand awareness-constructing process.
-
The effects of social media live streaming commerce on Vietnamese Generation Z consumers’ purchase intention
Thi Thuy An Ngo ,
Chi Thanh Bui ,
Huynh Khanh Long Chau ,
Nguyen Phuc Nguyen Tran
doi: http://dx.doi.org/10.21511/im.19(4).2023.22
Innovative Marketing Volume 19, 2023 Issue #4 pp. 269-283 Views: 1010 Downloads: 943 TO CITE АНОТАЦІЯSocial media live streaming commerce is an emerging and effective online shopping channel that integrates live streaming and e-commerce through social media platforms. This trend has gained significant attention, particularly from Generation Z, who are drawn to the interactive and entertaining aspects of shopping through live streaming. This study investigates factors affecting the purchase intention of Vietnamese Generation Z consumers in live streaming commerce on social media platforms, assessing the impact of six factors: entertainment, information quality, interactivity, perceived risk, peer customer evaluations and recommendations, and streamers. Using a non-probability sampling, an online survey was conducted among 344 consumers who possess prior experience with social media live streaming commerce. Data analysis used a partial least squares structural equation modeling technique. The findings revealed that increased entertainment, higher information quality, enhanced interactivity, positive peer customer evaluations and recommendations, and a more attractive and expert streamer positively impact purchase intention. Notably, streamers exhibited the most robust influence, while information quality demonstrated the weakest effect among the influencing factors. Conversely, perceived risk did not significantly hinder purchase intention, suggesting Generation Z consumers’ confidence in online transactions and their willingness to take risks for entertainment and interactivity in live streaming commerce. In light of these results, businesses are advised to elevate consumer purchase intentions by prioritizing aspects related to entertainment, information quality, interactivity, and peer customer evaluations. Prudent selection of streamers is highlighted as a pivotal strategy for organizations to effectively shape customer purchasing intentions.
Acknowledgment
The researchers express sincere gratitude to all the participants who generously participated in this study.
