1 Introduction
As an additional, fast-growing labor market, the gig economy has changed the way people work and is attracting increasing numbers of academic investigations (Jabagi et al.
2019; Kuhn
2016). In 2020, about 36% of the U.S. labor force (59 million people) worked as freelancers (Upwork
2020). Online freelancing plays an important role, especially in professions such as software engineering, digital marketing, design, image editing, writing and translating (Blaising et al.
2021). Moreover, the number of qualified freelancers is increasing, e.g. in the field of computer programming and IT (Upwork
2021).
Particularly for IT work, digital labor markets offer new opportunities to tackle the increasing need, the chronic skills shortage, high turnover rates and the growing talent gap in IT (Apfel et al.
2020; Fuller et al.
2020; Wiesche et al.
2019). IT freelancers perform software development work online as independent contractors rather than as employees of a permanent company (Sison and Lavilles
2018). The field of IT freelancing must be distinguished from other freelancing areas because IT work itself exhibits specific characteristics that demand further investigation in the context of digital labor markets. IT freelancers differ from other online freelancers in two main aspects.
First, the breadth of knowledge and skills required, the constant change and development, and the need to learn and adapt knowledge in an intellectually demanding work context are characteristics that distinguish the IT profession from other professions (Riemenschneider and Armstrong
2021). IT freelancers must particularly respond to the threat of skill obsolescence, especially in the context of digital labor platforms, as they face a high level of personal responsibility for continuous training, updating, and learning (Graham et al.
2017; Kost et al.
2020; Spreitzer et al.
2017). On digital labor platforms, these characteristics additionally pose a particular challenge to the success of IT freelancers, as they must further differentiate themselves from the global competition by meeting high skill requirements (Gandini
2016; Jarrahi et al.
2020). Compared to other freelancing fields, IT tasks are more complex, interdependent and constantly evolving (Stol and Fitzgerald
2014). In particular, the aspect of skill obsolescence is less relevant for freelancers in, for example, the areas of design or translation (Gussek and Wiesche
2022b).
Second, IT work often requires collaborative efforts in designing architectures and integrating components as well as teamwork (Ang and Slaughter
2001; Kudaravalli et al.
2017; Levina
2005; Majchrzak et al.
2005). On digital labor platforms, meanwhile, freelancers usually work alone and teamwork is not common, e.g., for image editing, translation, or simple design tasks (Ashford et al.
2018). But for IT freelancers, some collaboration or teamwork can boost careers and help with advancement (Gussek and Wiesche
2022b).
On the one hand, collaboration within organizations is becoming more intense (Maruping and Matook
2020), but IT workers also value freedom and self-determination in their careers (Gol et al.
2018). In comparison, the traditional understanding of career success refers to the perceived or actual achievements of individuals (e.g., Judge et al.
1999). To succeed in digital labor markets despite the described characteristics of IT work, however, IT freelancers must independently apply suitable strategies. Due to the digital organization of work, the geographical distribution of freelancers and clients, and the resulting strong competition, IT freelancers need to convince potential new clients through their profiles on a platform to acquire projects (Agrawal et al.
2015). To improve trust and reduce uncertainty among potential clients, IT freelances can display signals about their achievements and skills (Connelly et al.
2011; Hukal et al.
2020; Kathuria et al.
2021).
Previous studies on careers in the gig economy suggest that a high level of expertise as well as a wide range of skills and resources and self-expression are necessary for workers to be successful (Ashford et al.
2018; Damarin
2006; Petriglieri et al.
2019; Van den Born and Van Witteloostuijn
2013). Some studies have already investigated the sending of signals on online labor markets. Durward et al. (
2016) present signaling behavior as a mechanism that influences bargaining power and thus success in crowdsourcing. Yoganarasimhan (
2013) finds that buyers are predictive, that they place significant weight on seller reputation, and that failure to account for momentum and choice can bias estimates of reputation. Horton (
2019) examines the introduction of a new signaling feature into the context of a market design platform. In addition, Kathuria et al. (
2021) investigate the effectiveness of skill and achievement signals. Moreover, only a few studies have focused on IT work in the gig economy and examined, for example, IT crowdsourcing (Stol and Fitzgerald
2014; Taylor and Joshi
2019) or the characteristics of online software development freelancers (Sison and Lavilles
2018; Watson Manheim and Ahuja
2019). In combination, however, it remains unclear how signaling differs for freelancing in the IT field and in online labor markets given the specific characteristics of IT work and digital labor platforms described earlier. This makes the signaling of IT freelancers on digital labor platforms not well comparable to signaling in other fields.
We test signaling theory in the new context of digital labor platforms, investigating IT specifics, and proposing a new typology of signals. Therefore, this research seeks to answer the research question: How do different signals on digital labor platforms affect the career success of IT freelancers? Using a quantitative analysis of 7,166 IT freelancer profiles from the freelance platform Upwork, we investigate the relationship between the use of signals and the career success of IT freelancers. Specifically, we study how three types of signals influence the career success of IT freelancers: activating signals, pointing signals, and supporting signals. The activating signals are related directly to the person and illustrate their skills, characteristics and human capital independent of the digital labor platform (e.g., education or programming skills). The pointing signals describe some kind of behavior and are therefore not directly related to the person; they are specific to the platform market and refer to the presentation of a certain image to convince clients and stand out from the competition on the platform (e.g., ratings of platform clients or a profile badge). Finally, the supporting signals are related to beyond the person and therefore indicate a certain form of team or group support (e.g., agency support on the platform).
The rest of the paper is organized as follows. First, the theoretical background and the state of the literature are explained, which leads to the development of the hypotheses. We then describe the data, the sample and the methodology. Finally, we report the results, discuss the main findings, and conclude.
6 Discussion
Prior research calls for studying the influence of signal types and signal environment on the effectiveness of signals (Connelly et al.
2011; Durward et al.
2016; Kathuria et al.
2021). Our results advance this discussion by examining the impact of three signaling types in the context of digital labor platforms on the career success of IT freelancers. Our study suggests that different activating signals related to human capital, pointing signals related to image presentation specific to digital platforms and supporting signals positively predict the objective career success of IT freelancers on digital labor platforms.
This improves our understanding of the recent technological and economic changes in the employment environment. The workplace is experiencing rapid digitalization, exemplified by the digital labor platforms where new, digital forms of communication and work processes are the norm. This digital work execution and mediation leads to uncertainties between parties, which can be reduced by IT enabled signals and thus lead to success in these new digital environments. Thus, this paper shows how technology can be built and used on digital labor platforms, especially in the form of signals. However, we also illustrate that digital labor platforms still need to be improved regarding available signals to build technology for humanity.
6.1 A Typology of Signals to Explain Career Success of IT Freelancers on Digital Platforms
Previous research has mostly focused on signals that can be distinguished according to their associated costs (assessment and conventional signals) (Donath
2007; Holthaus and Stock
2018), or according to whether they are self-reported or from a third party (internal and external signals) (Mavlanova et al.
2016; Spence
1973). In addition, prior research lacks a consensus on signal effectiveness in online freelance job markets (Durward et al.
2016; Gefen and Carmel
2008; Hukal et al.
2020). The previous signal distinctions, therefore, are not sufficient for the context of IT work on digital labor platforms. Furthermore, since we focus on digital labor platforms in the IT domain, we investigated the support signal as a third new signal type relevant for IT freelancers. Therefore, we focused on three signaling types: activating signals, pointing signals, and supporting signals (Bianchi et al.
2019; Connelly et al.
2011; Durward et al.
2016; Schulz et al.
2015).
Our data show that all three signal types positively influence the success of IT freelancers. More specifically, the proof of skills in the IT field and the English skills are relevant for success. The indication of an additional currently requested IT skill on the IT freelancer profile can increase the earnings by 10.4%. In addition, the improvement of English skills by one level can even result in a 30% increase in earnings. Furthermore, the number of jobs in the work history and the resulting review quality also has a positive impact on the success of IT freelancers, as well as the completeness of the profile and the receipt of a profile badge for self-promotion. Finally, IT freelancers who receive and signal social support are more successful than those who work alone without any support (Exp(b) = 1.609, p < 0.01).
6.2 The Role of the Signaling Environment: The IT Domain on Digital Labor Platforms
Overall, the signaling environment is an under-researched aspect of signaling theory and only a few research papers have investigated the signaling context so far (Connelly et al.
2011; Kathuria et al.
2021). We contribute to this literature by testing signaling theory in the new context of IT work on digital labor platforms. The three types of signals presented must be considered in the environment in which they are used. Besides, it also plays a role who sends the signals. Therefore, this study also examined the applicability of signals in the digital labor platform environment and the IT domain.
Through our analysis, the interplay between the characteristics of online freelancing on digital labor platforms and the characteristics of IT work became clear. The use of signals is special in this context due to the nature of the work relationship because freelancers work independently and are thus responsible for their own career. Moreover, work coordination and execution occurs completely online via the digital platform. In addition, we were able to show that different IT characteristics affect the effectiveness of the signals.
First, the characteristics of online freelancing and IT work regarding activating signals become clear. We were able to show in this paper that IT freelancers need to build human capital themselves in order to be successful. Above all, this illustrates the diversity of skills needed. For example, we showed that general skills are important for success, like English skills (Exp(
b) = 1.300,
p < 0.01), while, simultaneously, the specific expertise for IT jobs, such as the amount of top IT skills the freelancer indicates in the considered year, is important for success (Exp(
b) = 1.104,
p < 0.05). However, we did not find that the amount of skills listed on the IT freelancer profiles significantly influences 1-year earnings. A possible explanation could lie in the associated costs for the signals. These are possibly higher for IT specific signals and thus have a stronger impact on success. Furthermore, from the results, it can be concluded that skills in the specific skill environment are essential for career success. IT work is driven by fast technological change, which leads to rapid knowledge obsolescence and a constant need for training and skill development (Zhang et al.
2012). We support these findings because signaling mastery of currently in-demand IT skills, in particular, had a positive effect on IT freelancers’ earnings. Lastly, we did not find that IT freelancers’ education significantly impacted earnings. A possible explanation for this result could be that IT work is constantly changing and IT freelancers do not necessarily learn the skills required for a project in their basic education. Therefore, the qualifications that had been acquired some time ago are not necessary for success. Consequently, anyone can enter the platform and be successful if they adapt their skills to current market demands. Concrete skills in the field of activity, such as the programming skills, are therefore particularly important (Fuller et al.
2022).
Second, the characteristics of online freelancing and IT work also become clear in terms of pointing signals. In the traditional labor market, the demand for IT professionals is very high. Moreover, the constant technological change makes it difficult to find replacements for IT professionals who have left a team (Bosworth et al.
2013; Thibodeau
2012). For this reason, image presentation on the digital platform and the associated use of platform-specific pointing signals is usually not necessary for IT professionals outside of digital labor platforms. However, within the context of digital labor markets, self-promotion is mandatory due to global competition and IT freelancers are self-responsible for this promotion (Ashford et al.
2018; Hennekam and Bennett
2016; Roberts
2005; Vallas and Christin
2018). IT freelancers need to deal with algorithmic labor management for a successful image presentation because career success especially depends on the logic of the algorithms on the digital labor platforms. Here, we contribute to the literature on the use and handling of algorithms and matching (Cram et al.
2020; Möhlmann et al.
2021; Straub et al.
2015). Freelancers are highly dependent on and driven by established metrics. For example, Uber measures availability, jobs accepted, jobs completed, and customer reviews (Kalleberg and Dunn
2016). Consequently, the platform’s mechanisms are important and must be used and influenced or addressed by IT freelancers. Regarding the platform-specific pointing signals concerning image presentation, we could show through our study that it is important for successful careers of IT freelancers to actively use certain mechanisms of the digital labor platform. The platform offers various possibilities, which IT freelancers should use. We observed significant effects from the number of completed jobs (Exp(
b) = 1.520,
p < 0.01), the quality of reviews in the work history (Exp(
b) = 1.081,
p < 0.1), the completeness of profile information (Exp(
b) = 1.196,
p < 0.01) and the achievement of a profile badge (Exp(
b) = 2.490,
p < 0.01). By using such signals, IT freelancers can move up the lists of available freelancers and thus be more visible to potential clients. In addition, it could be interesting to develop additional platform features for IT freelancers to support image development and more platform-specific pointing signals.
Third, the characteristics of online freelancing and IT work additionally become clear regarding supporting signals. In IT, collaboration, communication and teamwork are of great importance for the successful completion of tasks (Ghobadi and Mathiassen
2016; Kudaravalli et al.
2017; Meyer et al.
2021). Consequently, IT freelancers are also often interdependent during their work. This reality is made difficult on digital labor platforms and in the context of freelancing scenarios, so IT freelancers must make more efforts to build an active community and some support. This is because in traditional freelancing, work is usually done alone and freelancers are separated from each other and clients (Ashford et al.
2018; Kunda et al.
2002). We contribute to this literature by showing that IT professionals can be more successful in teams or groups than alone, even on digital labor platforms. Accordingly, the signaling of support from groups or agencies has a positive impact on an IT freelancer’s earnings (Exp(
b) = 1.609,
p < 0.01). This result provides a starting point for future research, as the investigated team or group support of IT freelancers can be used as a baseline for development of further supporting signals.
6.3 The Need to Improve Digital Labor Platforms for Humanity
Through our investigation, it became clear that digital platforms still need to be improved in terms of available signals to actually build “technology for humanity”. Surprisingly, we found that many signals used in traditional labor markets could not be observed in our dataset, as globalized, anonymous digital labor platforms do not provide space for this type of holistic assessment of a person. In Table
7, we have listed possible signals that might be particularly relevant on digital platforms within the three signal types in addition to those we found.
The detected and analyzed activating, pointing and supporting signals are very limited. Consequently, truly meaningful signals on digital platforms are very limited. In their current form, platforms enormously degrade the ways in which people can be evaluated. All the signals on the profiles tend to be transaction-based and signals related to individual situations and personality (e.g., Ashford et al.
2018; Van den Born and Van Witteloostuijn
2013) are not available. One possible reason could be that the inherent goal of digital platforms is to capture value (Schreieck et al.
2021). The information control of freelancers is therefore limited because they cannot communicate and signal many dimensions of their identity in any way on the digital platforms (Averill
1973). Thus, they have a very limited ability to influence or control the decisions of the people who assess them. Therefore, to build technology for humanity and be fairer, digital labor platforms would need to be improved in the future to give space to more relevant and differentiated signals.
6.4 Practical Contributions
This research has practical implications as well. First, our results on activating signals referring to human capital show that IT freelancers need to keep their skills up to date. The fast pace of information technology leads to rapid obsolescence of technologies and skills. Obsolescence is particularly relevant for IT professionals, which is why freelancers should invest heavily in their human capital to keep it current, especially in their professional domain. It also became clear that (IT) education has no significant impact on success. Consequently, platform work in the IT field is attractive for everyone, even if they were not educated in classic IT fields. Secondly, IT freelancers should consider and implement various factors of image presentation through platform-specific pointing signals. Our results show that it is crucial for the career success of IT freelancers to have a high review quality, many completed jobs in the work history, a profile badge and overall a complete profile. Therefore, they should actively fight for a good rating from the clients or a profile badge to stand out from the competition. Third, the results underline the importance of IT freelancers actively searching for support on the digital labor platform to receive content support for completing their tasks and an emotional network in order to be more successful. Lastly, digital labor platforms could solve the problem of high demand for IT professionals, as digital labor platforms always have many IT professionals available (Popiel
2017). Furthermore, the international use of the platform provides access to IT professionals from all over the world. Thus, the platforms are a potential new source of skilled IT workers for organizations.
6.5 Limitations and Future Research
Our research has some limitations that need to be considered. First, we only study IT freelancers on the platform Upwork, which means that we cannot draw a comparison between different digital labor platforms. In future work, it would be interesting to explore whether our results also apply to other online freelancing platforms, such as Fiverr. Second, we do not differentiate between different skills or the degree to which skills are mastered (depth) but only use the total amount of skills as a measure of human capital. Future research could therefore investigate whether some (IT) skills are more relevant than others for specific tasks and thus the degree to which various skills and the respective breadth and depth of a freelancer’s skillset influence career success. Third, through the profile data of IT freelancers, we can only study the objective career success. Therefore, the individual’s reactions to their career experiences should be considered in more detail in future work (Hughes
1937; Judge et al.
1995). Subjective success and other enriched insights into the careers of IT freelancers can be considered through targeted surveys or interviews. Therefore, more causal inferences could be drawn regarding the results. Fourth, the final analyzed dataset (7166 profiles) includes only a subset of the originally scraped freelancer profiles (36,661 profiles). This is mainly due to the choice of the dependent variable. In order to determine a meaningful value for objective success as a way to interpret the effect of the different signals, we use success over the one year period after the signals are sent. As a result, many profiles that left the platform market during this year or set their profiles to private for the second data collection point are not included in the analysis. In addition, there are many incomplete profiles. As with any empirical data collection of this kind, these circumstances could lead to a bias in the results due to survivorship bias. Lastly, other effects regarding IT freelancers’ career success could be considered, such as the application of different business strategies, gender or age. For example, a low-cost strategy or an industry specialization strategy could contribute to the success of IT freelancers on digital labor platforms (Van den Born and Van Witteloostuijn
2013). In addition, the issue of obsolescence is significant to the success of IT freelancers. It is exciting to investigate in the future how the strategy of unlearning works to ensure the ability to innovate and remain responsive (Matook and Blasiak
2020).