Stars in social media: new light through old windows?

ABSTRACT

We review the economics of superstars, originally developed for stars in traditional media, and discuss whether they are applicable for the novel phenomenon of stars in social media (influencer, micro-celebrities). Moreover, we analyse potentially new factors for creating social media superstardom. Our overall result is that the economics of superstars remain applicable and relevant for social media stars. In line with this assessment, we find that several (allegedly) new star factors in social media, like disappearance of gatekeepers and authenticity, turn out to be just slightly different to traditional concepts. However, algorithm management and upload strategies represent novel success factors relevant for social media superstardom that are not captured by traditional superstar theories.

KEYWORDS:

• Social media
• digital media
• influencer
• popularity
• superstars
• media economics

Acknowledgments

We thank the participants of the annual conference of the European Media Management Association (EMMA; Limassol/Cyprus, June 2019) and the 52nd Hohenheimer Oberseminar (Weimar, April 2019) as well as Christopher Buschow and Barbara Ann Güldenring for valuable comments on earlier versions of the paper. Furthermore, we thank Elena Willi, Milena Wehner and Alina Ahrens for valuable editorial assistance.

Disclosure statement

We have no potential conflict of interest.

Notes

1. See Kaplan and Haenlein (2010) and Detel (2017, p. 119) for further differentiation between social media platforms.

2. However, measuring individual performance in team sports is more difficult: does the number of goals of a striker in (European-style) football, for instance, measure his performance (and talent) or the one of his team and/or the centres with their passes?

3. However, if star-fan interaction is important, the cost of communication and interaction increases with output quantities (audience size) (see section 3.4).

4. For star-fan communication see section 3.4; for influencer marketing and corporate communication through social media stars see Enke and Borchers (2019).

For star-fan communication see section 3.4; for influencer marketing and corporate communication through social media stars see Enke and Borchers (2019).

5. Hashtags are keywords or catchwords used in social media, which are created by placing the sign “#” directly in front of it. These words are intended to convey the key message of the content and simplify the possibility to find the respective content (and similar posts).

6. Different aspects on algorithms can be found in scientific literature. On the demand side, digital personal assistants (Budzinski et al., 2019a), algorithmic consumers (Gal & Elkin-Koren, 2017) and bots (software robots, which are designed to exhibit human-like behaviour) (Ferrara, 2015; Ferrara et al., 2016) can be named. On the supply side, price-setting algorithms raised concerns of novel competition problems and collusive behaviour among competitors (Ezrachi & Stucke, 2015, 2016a, 2016b, 2017; Mehra, 2015; Oxera, 2017; Pasquale, 2016; Schwalbe, 2018; Woodcock, 2017).

7. On data-based services, personalised data and privacy also see Acquisti & Varian (2005), Acquisti et al. (2016), Brown (2016), Budzinski and Kuchinke (in press), and Budzinski and Stöhr (2019).

8. These platforms can be characterised as artificial platforms. A business model based on subscription fees would also be possible but is not chosen, presumably to attract users and initiate network effects.

9. Cross-referencing and networking means linking other social media stars in the content, generating joint content or being featured on other social media accounts.

10. On economics of attention markets also see Evans (2013, 2017), Boik et al. (2017); and building on Becker (1965), Goolsbee and Klenow (2006), and Brynjolfsson and Oh (2012).

11. For instance, Google increased the requirements for monetarisation of YouTube accounts: “Once a channel reaches 4,000 watch hours in the previous 12 months and 1,000 subscribers it will be reviewed to join the program”. (Google, 2018).

12. In 2015, 70–80% of the top 100 channels in Germany cooperated with MCNs (Roß & Weghake, 2015). Unfortunately, newer information is barely available. Socialblade, for instance, stopped providing information on MCNs since they now engage in YouTube consulting themselves.

13. In the main part of the study, 700 German teenagers between 13 and 19 answered an online questionnaire with regard to different characteristics as, inter alia, role model function, authenticity and credibility.

14. Closely connected to authenticity is credibility, since the stars seem trustworthy, realistic and honest. See Xiao et al. (2018) for influencer credibility, social media marketing and brand attitude.

15. While the narrative of “ordinary people stars” may have some credibility for the early days of social media, the professionalisation of social media entertainment is, by now, a well-established development (inter alia, Cunningham et al., 2016; Döring, 2014; Kim, 2012).

16. A popular example is the YouTuber Pewdiepie (Felix Kjellberg) who posted anti-Semitic videos and caused YouTube and Disney to cut ties with him (Winkler et al., 2017).

Additional information

Notes on contributors

Sophia Gaenssle

Sophia Gaenssle is junior researcher at the chair of Economic Theory at the Ilmenau University of Technology since 2017. She specialises in media and cultural economics, with particular emphasis on industrial organisation and digital markets.

Oliver Budzinski

Oliver Budzinski is a full professor of economic theory and the director of the Institute of Economics at Ilmenau University of Technology, Germany. His research areas include industrial economics and competition policy, media and cultural economics as well as sports economics. He has published three books, more than 40 articles in peer-reviewed academic journals and more than 40 chapters in handbooks and other collected volumes.