‘If He Just Knew Who We Were’:Microworkers’ Emerging Bonds of Attachment in a Fragmented Employment Relationship

Using the lens of attachment, we explore microworkers’ views of their employment relationship. Microwork comprises short-term, task-focused exchanges with large numbers of end-users (requesters), implying transitory and transactional relationships. Other key parties, however, include the platform which digitally meditates worker-requester relationships and the online microworker community. We explore the nature of attachment with these parties and the implications for microworkers’ employment experiences. Using data from a workers’ campaign directed at Amazon Mechanical Turk and CEO Jeff Bezos, we demonstrate multiple, dynamic bonds, primarily, acquiescence and instrumental bonds towards requesters and the platform, and identification with the online community. Microworkers also expressed dedication towards the platform. We consider how attachment buffers the exploitative employment relationship and how community bonds mobilise collective worker voice

Digital workers by design? An example from the on-demand economy. CEPS Working Document No. 414/October 2015 Monday, 19 October 2015

Recent organisational and technological changes à la Uber have generated a new labour market fringe: a digital class of workers and contractors. In this paper we study the case of CoContest, a crowdsourcing platform for interior design. Our objective is to investigate how profitable this type of work can be, also from a cross-country perspective, and why professionals choose to supply work on such a platform. Given the low returns, one might expect to see a pattern of northern employer/southern contractor. Yet analysis reveals a more nuanced pattern, in which designers supply their work even if they live in Italy, which is a high-income country. For these designers work on CoContest can make sense if they are new to the labour market and face high entry barriers, although crowdsourcing does not offer them profitable employment full time. The case of Serbia, the second-largest supplier of designers, is different, however. As a result of differences in purchasing power, if the market grows experienced Serbian designers can expect to make a living from crowdsourced contracts

Reputation Agent: Prompting Fair Reviews in Gig Markets

Our study presents a new tool, Reputation Agent, to promote fairer reviews from requesters (employers or customers) on gig markets. Unfair reviews, created when requesters consider factors outside of a worker's control, are known to plague gig workers and can result in lost job opportunities and even termination from the marketplace. Our tool leverages machine learning to implement an intelligent interface that: (1) uses deep learning to automatically detect when an individual has included unfair factors into her review (factors outside the worker's control per the policies of the market); and (2) prompts the individual to reconsider her review if she has incorporated unfair factors. To study the effectiveness of Reputation Agent, we conducted a controlled experiment over different gig markets. Our experiment illustrates that across markets, Reputation Agent, in contrast with traditional approaches, motivates requesters to review gig workers' performance more fairly. We discuss how tools that bring more transparency to employers about the policies of a gig market can help build empathy thus resulting in reasoned discussions around potential injustices towards workers generated by these interfaces. Our vision is that with tools that promote truth and transparency we can bring fairer treatment to gig workers.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202

Grounds for a strike: South African gold mining in the 1940s

Paper presented at the Wits History Workshop: The Making of Class, 9-14 February, 198

The Challenges of Crowd Workers in Rural and Urban America

Crowd work has the potential of helping the financial recovery of regions traditionally plagued by a lack of economic opportunities, e.g., rural areas. However, we currently have limited information about the challenges facing crowd work-ers from rural and super rural areas as they struggle to make a living through crowd work sites. This paper examines the challenges and advantages of rural and super rural AmazonMechanical Turk (MTurk) crowd workers and contrasts them with those of workers from urban areas. Based on a survey of421 crowd workers from differing geographic regions in theU.S., we identified how across regions, people struggled with being onboarded into crowd work. We uncovered that despite the inequalities and barriers, rural workers tended to be striving more in micro-tasking than their urban counterparts. We also identified cultural traits, relating to time dimension and individualism, that offer us an insight into crowd workers and the necessary qualities for them to succeed on gig platforms. We finish by providing design implications based on our findings to create more inclusive crowd work platforms and tool

TurkScanner: Predicting the Hourly Wage of Microtasks

Workers in crowd markets struggle to earn a living. One reason for this is that it is difficult for workers to accurately gauge the hourly wages of microtasks, and they consequently end up performing labor with little pay. In general, workers are provided with little information about tasks, and are left to rely on noisy signals, such as textual description of the task or rating of the requester. This study explores various computational methods for predicting the working times (and thus hourly wages) required for tasks based on data collected from other workers completing crowd work. We provide the following contributions. (i) A data collection method for gathering real-world training data on crowd-work tasks and the times required for workers to complete them; (ii) TurkScanner: a machine learning approach that predicts the necessary working time to complete a task (and can thus implicitly provide the expected hourly wage). We collected 9,155 data records using a web browser extension installed by 84 Amazon Mechanical Turk workers, and explored the challenge of accurately recording working times both automatically and by asking workers. TurkScanner was created using ~150 derived features, and was able to predict the hourly wages of 69.6% of all the tested microtasks within a 75% error. Directions for future research include observing the effects of tools on people's working practices, adapting this approach to a requester tool for better price setting, and predicting other elements of work (e.g., the acceptance likelihood and worker task preferences.)Comment: Proceedings of the 28th International Conference on World Wide Web (WWW '19), San Francisco, CA, USA, May 13-17, 201

A Suggested Strategic Roadmap for Public Egyptian Universities to Adopt and Adapt to the Requirements of the Fourth Industrial Revolution and Society 5.0 to Prepare Students for the Future Labor Market

Currently, the world is facing an unprecedented challenge which is “youth bulge” with a high rate of unemployment. It is argued that the employability challenges will be compounded by the impacts of the Fourth Industrial Revolution (4IR) and Society 5.0, and “Open talent economy” is the new economy replacing traditional permanent employees with talented “free lancers” to perform the required work from anywhere in the world. Currently, institutions around the world prefer to employ those who have skills for specific projects. Gig economy primarily depends on two forms of work: “crowd work” and “work on-demand”. In addition, in the last five years, "artificial intelligence" (AI) has begun to replace people in many of routine jobs, and will continue to replace people in new unimaginable jobs that may arise in the future due to the continuous developments of smart technologies. Accordingly, universities all over the world will face a new problem of preparing students for a new way of life and work with a somewhat uncertain future in the coming era that involves a new industrial revolution whose repercussions are unprecedented.Accordingly, there is increasing trend that calls for the necessity of preparing a flexible or liquid workforce able to constantly adapt itself to the requirements of fast-changing labor market, and establishing a culture of flexibility in moving between businesses according to the needs of the labor market which witnessed the emergence of new types of labor across the world. If universities failed to align employability competences with the requirements of 4IR and Society 5.0, the unemployment gap will increase. Thus, the main objective of this study is to propose a strategic roadmap for public Egyptian universities to adopt and adapt to the requirements of the fourth industrial revolution and society 5.0 to prepare students for the future labor market. To achieve this objective, the author adopted both descriptive and exploratory research design, and used mixed methods research approach. Since the requirements of 4IR and Society 5.0 and Gig economy do not depend on national educational institutions that operate in accordance with national standards, but reliance is on the standards set by transnational "digital institutions" and platform companies, so the national contexts will have minor influence on practices and structure of university education systems, therefore the suggested strategic proposal may be applied by many universities in different educational contexts. Keywords: The Fourth Industrial Revolution; Society 5.0; Strategic Roadmap; Education 4.0; Work 4.0; Egyptian Universities; Gig economy. DOI: 10.7176/JEP/11-29-03 Publication date:October 31st 202

Information in online labour markets

Online labour markets are virtual platforms that solve information problems to enable gains from trade in remote labour services. They make employers and workers aware of each other, and allow them to communicate, contract, and produce remotely. Recent research suggests, however, that organizing production to include remote work remains challenging because employers and workers in these markets continue to lack information that is less easily communicated. Employers appear unable to accurately anticipate the full costs and benefits to them of using the market prior to entry, and continue to have difficulty evaluating worker applications even when experienced in these markets. Information is particularly incomplete when wage arbitrage opportunity is greatest