Selected Papers of #AoIR2024: The 25th Annual Conference of the Association of Internet Researchers Sheffield, UK / 30 Oct - 2 Nov 2024 Suggested Citation (APA): Whalley, J., Stocker, V., & Lutz, C. (2024, October). Freelancing in the Digital Age: Understanding Fiverr within the Gig Economy. Paper presented at AoIR2024: The 25th Annual Conference of the Association of Internet Researchers. Sheffield, UK: AoIR. Retrieved from http://spir.aoir.org. FREELANCING IN THE DIGITAL AGE: UNDERSTANDING FIVERR WITHIN THE GIG ECONOMY Jason Whalley Northumbria University, Volker Stocker Weizenbaum Institute, Christoph Lutz BI Norwegian Business School, Introduction and Methods In recent times, a series of platforms have emerged as key actors within the economy. ‘Big tech’ companies like Alphabet, Meta or Alibaba have developed into powerful and complex multi-layered platform ecosystems (Van Dijck et al., 2018). These firms dominate their respective markets, creating value for their users and shareholders, but also raising substantial concerns. Reflecting their size and scope, these platforms are often in the headlines and have attracted the attention of scholars and policymakers (Cabral et al., 2021; Crémer et al., 2019; Furman et al., 2019). Arguably they overshadow the development of smaller platforms that have emerged in the sharing economy and gig economy (Van Dijck et al., 2018). Within the sharing economy, platforms allow resources to be flexibly shared in areas like accommodation (e.g., Airbnb, Couchsurfing), car sharing (e.g., Share Now, BlaBlaCar) and storage and parking (e.g., stashbee). In the gig economy, by contrast, individuals rent out their labour (De Stefano, 2016; Frenken and Schor, 2017). Despite the considerable body of literature on the topic (Koutsimpogiorgos et al., 2020; Vallas & Schor, 2020), how specific gig economy platforms have developed and how they align buyers (clients) with sellers (freelancers) has so far been largely overlooked. Consequently, in this paper we will focus on Fiverr, a key gig economy platform. Extant research in the area has focused strongly on local gig work or app-work (Duggan et al., 2020; Vallas & Schor, 2020), especially in the form of ride-hailing platforms such as Uber and Lyft (Cameron, 2022; Rosenblat, 2018) or food delivery platforms such as Deliveroo, Doordash, Meituan and Foodora (Galière, 2020; Newlands, 2022; Van Doorn & Chen, 2021). Moreover, microwork or microtasking (Vallas & Schor, 2020) has been widely studied, with ample research about Amazon Mechanical Turk (Bucher et al., 2019, 2024; Newlands & Lutz, 2021). Finally, there is a rich body of research on capital platform work (Duggan et al., 2020), especially Airbnb (e.g., Dann et al., 2019). Much less evidence is available about online freelancing, an important sub-sector of the gig economy Vallas and Schor (2020) call “creative projects (doing, designing, and analysis)” and Howcroft and Bergvall-Kåreborn (2019) label “profession-based freelance crowdwork”. Fiverr specifically, despite its size and importance, has received scant attention but is a relevant case study for several reasons. As a global marketplace that connects sellers with buyers, Fiverr presents rich insights into the dynamics of cross-cultural digital work. Through its broad coverage of activities, Fiverr enables the study of changing patterns of knowledge work across industries. Moreover, the adaptability of Fiverr during COVID- 19, when it experienced a significant increase in demand, underlines its critical role in the gig economy’s response to the global pandemic. However, the emergence of generative AI represents a challenge to Fiverr’s business model, offering an interesting case how knowledge work and competitive dynamics in the gig economy adapt to new social realities. Investigating Fiverr’s development thus helps better understand and contextualize the gig economy and future of work. To explore how Fiverr has developed, we adopt a longitudinal case study approach (Eisenhardt, 1989; Yin, 2018). The case study combines materials by Fiverr itself (annual reports, regulatory filings with the Securities & Exchange Commission, press releases and presentations) with reports from newspapers, the trade press and analysts as well as academic literature. These sources were triangulated to construct a timeline of Fiverr’s development. This use of secondary sources is consistent with research that has examined the development of platforms in sectors such as textiles (e.g., Schmidt et al., 2021) and high technology (e.g., Ansari et al., 2016; Hein et al., 2019). Results and Discussion Our analysis shows that Fiverr remains an important platform in the gig economy, mirroring the broader emergence of digitally enabled platforms. These platforms not only bridge geographical divides but also accommodate a multitude of languages, thereby broadening their service range (Stephany et al., 2021). Despite Fiverr's expansion and improvements in operational performance, the benefits sellers can draw from the platform are unevenly distributed, with a substantial portion of sellers earning minimal returns. Specifically, 96% of sellers make less than $500 per month, with 70% earning less than $100 per month (Priceonomics, 2017). Strategically, Fiverr has extended its scope and capabilities over time, focusing on technology acquisition rather than talent, a practice known as acquihire. This strategy aligns with a larger digital platform trend of seeking competitive advantage through technological acquisitions rather than through talent integration (Parker et al., 2021). Our analysis shows how Fiverr has developed its business across more services, also highlighting how the platform is thematically structured, based on three classification schemes: First, categories describe relatively narrow task areas such as ‘photoshop editing’ and ‘tattoo design’. Second, verticals are broader domains, spanning multiple categories. An example is ‘graphics and design’ and currently there are nine verticals. Finally, Fiverr launched nine so-called stores, which are similar to verticals in breadth but encompass different task types or fields such as gaming, real estate, logo making, and e-commerce. The most recent store is ‘International Women Day’, where all of the services are provided by women. The introduction of this store seems pragmatically motivated, showing how gig economy platforms such as Fiverr hijack social justice issues such as gender inequalities for profit. Fiverr’s strategic orientation has led to a dynamic that predominantly favors buyers over sellers, aiming to maximize transactions and revenue. The platform’s pricing strategy contributes to this imbalance. Specifically, the buyer (client) pays a service fee determined by the size of the transaction, while the seller pays a transaction fee of 20 per cent. For example, if a gig is listed at $100 then the company earns $5 from the buyer and $20 from the seller. In other words, the buyer pays $105 to purchase the gig, from which Fiverr earns $25 while the seller receives $80. Our case study also shows the critical role of technology, especially machine learning, in facilitating transaction efficiency and platform scalability. Nonetheless, the success of such strategies significantly depends on the platform's adaptation to geographical and contextual conditions, which influence its expansion. These factors range from socioeconomic realities and regulatory frameworks to digital literacy levels and connectivity infrastructure, all of which dictate the platform’s ability to attract and retain buyers and sellers. For example, given Fiverr's Israeli roots, geopolitical sensitivities could impact its market reach and operational stability, although such effects may be mitigated by the platform's global footprint and economic imperatives driving its use. Additionally, Fiverr faces the challenge of disintermediation, where the platform develops strategies to maintain user engagement and prevent market bypassing. By positioning itself as an asset-light "inverted firm," Fiverr effectively scales its operations to meet the demands of an expanding user base without diluting service quality or alienating stakeholders (Parker et al., 2017). As a final note, Fiverr currently faces challenges from the rise of generative AI, for example ChatGPT, Midjourney, Gemini, and DALL·E. First empirical insights suggest that online freelancers – and particularly those engaged in language- and text-oriented work – are indeed exposed to automation risks (Demirci et al., 2023; Hui et al., 2024; Liu et al., 2023). References Ansari, S., Garud, R., & Kumaraswamy, A. (2016). The disruptor’s dilemma: TiVo and the US television ecosystem. Strategic Management Journal, 37(9), 1829-1853 Braesemann, F., Stephany, F., Teutloff, O., Kässi, O., Graham, M., & Lehdonvirta, V. (2022). The global polarisation of remote work. PLOS ONE, 17(10), e0274630. Bucher, E., Fieseler, C., & Lutz, C. (2019). Mattering in digital labor. 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