工作组织、劳工与全球化 第 17 卷，第 2 期，2023 年

全球数字劳动力平台上的数字声誉、技能和不确定性降低

尼尔斯·比雷普特、巴特·兰布雷茨和乔里安·奥普林斯

尼尔斯·比雷普特（Niels Beerepoot）是荷兰阿姆斯特丹大学阿姆斯特丹社会科学研究所的副教授。

Bart Lambregts是泰国曼谷泰国农业大学社会科学学院的讲师 Jorien Oprins是荷兰阿姆斯特丹大学阿姆斯特丹社会科学研究所的博士候选人

摘要 数字劳动力平台在一系列数字可转让服务贸易中变得越来越普遍。为了帮助参与者减轻在数字平台上交易固有的不确定性，反馈机制已成为衡量平台参与者“执行”质量和可靠性的主要工具。基于对 750 份书面反馈文本的分析，本文首先研究了哪些自由职业者素质（技术技能、通用技能或个人能力）对客户最重要，因此有助于在平台上建立自由职业者的数字声誉，其次，这些反馈文本究竟如何帮助减少通过平台进行交易时的不确定性。因此，本文加深了对数字劳动平台“游戏规则”的理解。

关键词 数字劳动平台、数字声誉、通用技能、零工、在线自由职业者

介绍

劳资关系的持续灵活性，主要是由经济生产本身的灵活性推动的，再加上生产者希望绕过受薪雇员的高成本和工人改变生活方式的选择，推动了

DOI：10.13169/workorgalaboglob.17.2.0007

工作组织、劳工与全球化 第 17 卷，第 2 期，2023 年

现在被称为“自由职业者”或“零工”就业（Graham，Hjorth和Lehdonvirta，2017;法博，卡拉诺维奇和杜科娃，2017年;Lehdonvirta 等人，2019 年）。数字劳动力平台已成为促进自由职业劳动力供需中介的重要工具（见Beerepoot&Lambregts，2015;Graham，Hjorth和Lehdonvirta，2017）。通过提供更先进的匹配机制、质量监控系统和支付工具，数字劳动力平台在一定程度上降低了交易壁垒，即使是小型、低预算任务的外包也变得具有成本效益（Beerepoot & Lambregts，2015）。许多数字劳动力平台为当地劳动力市场提供相对直接的匹配服务（例如TaskRabbit、Uber、Deliveroo）。其他公司，如Freelancer、Upwork和Fiverr，超越了地理界限，旨在在全球范围内将自由职业者的供需联系起来。这些全球平台通常围绕现代服务活动展开，如应用程序制作、网站和平面设计、虚拟协助、翻译和转录、电话营销和创意写作（参见Wood，Graham，Lehdonvirta和Hjorth，2019），这使它们与专门用于图像标记和调查等微任务服务的平台区分开来（例如，Amazon Mechanical Turk）。正式的进入门槛通常很低，这意味着任何识字、拥有计算机和互联网的人都可以注册并使用此类平台来寻找或提供电子传输服务。它们的受欢迎程度正在上升，目前全球有数千万自由职业者使用两个最大的平台（Freelancer 和 Upwork）（参见 Lehdonvirta 等人，2019 年）。 通过提供灵活的、技术支持的就业，数字劳动力平台已成为更广泛地被视为未来工作的关键要素（见Berg，Furrer，Harmon和Silberman，2018;世界银行，2019年;国际劳工组织，2021a）。

然而，在此类平台上进行交易充满了不确定性。客户（劳动力的购买者）和自由职业者（劳动力的卖家）不太可能相互认识，并且可能彼此相距数千英里，并且来自非常不同的文化和教育背景（Beerepoot&Lambregts，2015;Graham，Hjorth和Lehdonvirta，2017）。此外，与商品贸易不同，在此类平台上交易的产品（各种服务）的质量在生产和交付之前不容易评估（Yoganarasimhan，2013）。为了帮助参与者减轻数字劳工平台上交易的不确定性，这些平台通常引入了反馈和声誉机制。这些工具通过使用用户满意度数据（通常是工作满意度分数和客户给自由职业者的反馈文本的组合，反之亦然）来衡量自由职业者和客户的“执行”质量和可靠性（Einav，Farronato&Levin，2015）;Galperin，2021 年）。在全球数字劳动力平台通常存在的买方市场中，数字声誉与高效的搜索和比较工具相结合，使客户更容易识别，选择和雇用最知名的自由职业者（Stanton&Thomas，2014）。由此产生的分布和排斥效应（Gawer & Srnicek，2021 年;Braesemann 等人，2022 年），这意味着建立和培养良好的数字声誉对于（尤其是）自由职业者在此类平台上取得成功至关重要。

Work organisation, labour & globalisation Volume 17, Number 2, 2023

Against this backdrop, this article addresses two themes. First, the article investigates which combination of skills it takes for freelancers to build a good digital reputation in such crowded, competitive and heterogeneous online arenas. Second, it explores what feedback texts tell us about uncertainty and uncertainty reduction when trading via digital labour platforms. Building on Sutherland, Jarrahi, Dunn and Nelson (2020:465), who observed that ‘building a reputation is a complicated process, which requires not only producing quality work, but also developing the social and technical skills to handle temperamental clients and algorithms’, and on Wood, Graham, Lehdonvirta and Hjorth (2019), who found that skills and platform reputation are the most important individual resources for online gig work, this article develops a detailed understanding of which particular skills, as Hearn (2010:427) calls them, ‘embody the values of their working environment’. In the recently emerging, and diverse scholarship on digital reputation (see, for instance, Yoganarasimhan, 2013; Gandini, 2016; Mikołajewska-Zając, 2018; Van Dijck, Poell & De Waal, 2018), the question of which specific skills combinations matter for successful performance on particular platforms, and hence the building of a digital reputation, remains unexplored.

We investigated the above question by analysing 750 feedback texts (formulated by clients) for two samples of freelancers working via one of the largest global digital labour platforms (375 web developers, representing a highly skilled service category, and 375 administrative support providers, representing a low-skill service category). We ascertained which skills appear most frequently in those feedback texts and which skill combinations are valued most. Next, we looked at the feedback texts more closely and examined what strategies clients use to reduce uncertainty in their transactions. The combined results help to achieve a deeper understanding of the ‘rules of the game’ on digital labour platforms.

The article is structured as follows: the next section develops the analytical framework for the study. It further examines the significance of uncertainty reduction in digital labour platforms and presents a typology of skills for competitiveness. The following section presents the conceptual model, details the methodology and discusses the data collection and analysis. A fourth section investigates which skills are key to building a digital reputation on digital labour platforms and a fifth examines how feedback texts help in the reduction of uncertainty on digital labour platforms. Conclusions are drawn in the final section.

Theoretical framework

Uncertainty on digital labour platforms

Competition on digital labour platforms is not only intense but also, because of the diversity of the actors involved, a complex game. Clients posting a job on such platforms are likely to be presented with a motley collection of freelancers making a bid, some of whom are likely to be based, raised and educated somewhere half a world away in a profoundly different socio-cultural and educational context than the client’s (see Beerepoot & Lambregts, 2015; Graham, Hjorth & Lehdonvirta, 2017). This creates an assessment challenge for clients (Pallais, 2014), and adds a measure of uncertainty to hiring transactions. More uncertainty is created by the digital nature of

Work organisation, labour & globalisation Volume 17, Number 2, 2023

and adds a measure of uncertainty to hiring transactions the working relation between clients and freelancers. With clients and freelancers located at a distance and the chances of them ever meeting in person being slim, the embedded relations and social monitoring and sanctioning mechanisms that in onsite working environments encourage people to perform well and live up to their promises, carry less weight, making it easier for either freelancers or clients to disengage or otherwise fail their counterparts (Einav, Farronato & Levin, 2015). As Yoganarasimhan (2013:860) argues,

buyers face considerable risks in these marketplaces – sellers may deliver lowquality services, abscond with advance payments, hold up the job without completing it and/or delay it, and steal intellectual property given to them during the job and sell it to a competitor or use it themselves.

Such conditions make digital labour platforms a good example of markets where, in the words of Beckert (2020:286), ‘product quality cannot be known in the present because it depends on future developments, which are not yet knowable’.

It is this uncertainty about what qualities and behaviours marketplace participants will bring to a job that fuels the search for, and reliance on, mechanisms to reduce it. Rational market participants, after all, will be seeking to enter into a transaction at minimum risk, and therefore look for counterparts with a proven record of competence, trustworthiness and other relevant qualities (see Bolton, Katok & Ockenfels, 2004). Most digital labour platforms try to reduce uncertainty by enhancing transparency. They usually perform little upfront screening or certification, but instead rely on reputation and feedback mechanisms, used in combination with efficient search and compare tools, to maintain quality (Einav, Farronato & Levin, 2015). Participants have access to information – objective and subjective – about each other’s capabilities and trustworthiness (Hagiu & Rothman, 2016). Objectified information about

freelancers’ skills is produced by enabling freelancers to have their skills uniformly tested, with test scores then being added to a freelancer’s profile and made visible to clients and to other freelancers. Subjective information is generated by encouraging clients and freelancers to evaluate each other after a job has been completed. The ‘user satisfaction’ data so produced is usually composed of a job success rate score and the written feedback freelancers (or clients) have received from previous clients (or freelancers) they have worked with. It reflects, at least in principle, the genuine and de facto experiences of past customers. While the job success rate scores allow for quick and easy comparison, the text comments tend to contain the richer, more fine-grained information about market participants’ performance (e.g. reliability, commitment, responsiveness) that cannot be conveyed by numbers (Pavlou & Dimoka, 2006). The two in combination amount to what is known as a platform participant’s ‘digital reputation’ (see also Bellesia, Mattarelli, Bertolotti & Sobrero, 2019; Galperin, 2021).

Reputational systems on digital platforms, however, have their limitations. Given that most platforms represent buyers’ markets, freelancers are generally more pressured to maintain a well-developed reputation than clients, and thus find themselves in a position of dependency (Kinder, Jarrahi & Sutherland, 2019). The feedback itself may be compromised as well. It is vulnerable to manipulation as lack of oversight of the authenticity and legal continuity of the market participants is not exceptional on digital