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This paper aims to explore workplace learning practices within two types of crowdwork – microwork and online freelancing. Specifically, the paper scopes and compares the use of workplace learning activities (WLAs) and self-regulatory learning (SRL) strategies undertaken by microworkers (MWs) and online freelancers (OFs). We hypothesised that there may be quantitative differences in the use of WLAs and SRL strategies within these two types of crowdwork, because of the underpinning differences in the complexity of tasks and skill requirements.

To test this hypothesis, a questionnaire survey was carried out among crowdworkers from two crowdwork platforms – Figure Eight (microwork) and Upwork (online freelancing). Chi-square test was used to compare WLAs and SRL strategies among OFs and MWs.

Both groups use many WLAs and SRL strategies. Several significant differences were identified between the groups. In particular, moderate and moderately strong associations were uncovered, whereby OFs were more likely to report (i) undertaking free online courses/tutorials and (ii) learning by receiving feedback. In addition, significant but weak or very weak associations were identified, namely, OFs were more likely to learn by (i) collaborating with others, (ii) self-study of literature and (iii) making notes when learning. In contrast, MWs were more likely to write reflective notes on learning after the completion of work tasks, although this association was very weak.

The paper contributes empirical evidence in an under-researched area – workplace learning practices in crowdwork. Crowdwork is increasingly taken up across developed and developing countries. Therefore, it is important to understand the learning potential of this form of work and where the gaps and issues might be. Better understanding of crowdworkers’ learning practices could help platform providers and policymakers to shape the design of crowdwork in ways that could be beneficial to all stakeholders.

Underlying much recent development in data science and artificial intelligence (AI) is a dependence on the labour of precarious crowdworkers via platforms such as Amazon Mechanical Turk. These platforms have been widely critiqued for their exploitative labour relations, and over recent years, there have been various efforts by academic researchers to develop interventions aimed at improving labour conditions. The aim of this paper is to explore US-based crowdworkers’ views on two proposed interventions: a browser plugin that detects automated quality control “Gold Question” (GQ) checks and a proposal for a crowdworker co-operative.

The authors interviewed 20 US-based crowdworkers and undertook a thematic analysis of collected data.

The findings indicate that US-based crowdworkers tend to have negative and mixed feelings about the GQ detector, but were more enthusiastic about the crowdworker co-operative.

Drawing on theories of precarious labour, this study suggests an explanation for the findings based on US-based workers’ objective and subjective experiences of precarity. The authors argue that for US-based crowdworkers “constructive” interventions such as a crowdworker co-operative have more potential to improve labour conditions.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

The two core types of work in crowdworking differ in certain respects. However, similar learning activities and strategies are key to success in both cases making it imperative to gain greater knowledge of the practices. Gaining such insight will aid future development of crowdworking and subsequently help increase its impact to the advantage of different stakeholders involved.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

As conventional practices of working continue to be modified in the gig economy, more theoretical work examining the experiences of gig workers is needed. Relying on person-based fit and levels of analysis literature, this paper proposes an adaptation to the traditional Attraction-Selection-Attrition (ASA) framework to the gig economy.

Drawing on the ASA framework, this conceptual paper explores how gig workers join, leave and could be retained by gig employers.

The authors recognize an intermediary “organizing” phase within the ASA framework for gig workers. Using examples of appwork and crowdwork, the authors show that workers tend to self-organize through third-party websites to help gig work become economically sustainable, avoid being exploited and enhance gig workers' sense of community and identity.

The practical implications of this research lie in gig employers understanding how workers experience gig employment and in helping employers be successful in attracting, selecting and retaining quality workers and thereby lowering permanent attrition.

The authors propose a novel adaptation to the conventional ASA framework to include organizing as a phase in gig worker employment. This research defines gig attraction and attrition at the individual-level, selection at the individual- and task-levels based in person-job (PJ)-fit and the various aspects of gig organizing as encompassing fit with one's job, organization, and environmental (i.e., PJ-, PO-, PE-fit) at the individual-, task-, and network-levels.

Online gig labor platforms bring together a global and fast-growing workforce to complete highly granular, remote and decontextualized tasks. While these environments might be empowering to some workers, many others feel disenfranchised and removed from the final product of their labor. To better understand the antecedents of continued participation in forms of crowdsourced digital labor, the purpose of this paper is to explore the relationship between worker’s ability to create a narrative of their work mattering regardless, and their continued work engagement (WE) in these work setups.

The authors approach the relationship between individual mattering and digital WE through a longitudinal study among workers on the crowdworking platform Amazon Mechanical Turk. The authors further provide qualitative insight into individual perceptions of mattering based on essay data.

The authors develop a measure of mattering in crowdworking with four dimensions: reliance, social recognition, importance and interaction. Reliance is the most pronounced dimension, followed by interaction, importance and social recognition. In the final longitudinal model, only importance affects WE positively, while the other three mattering dimensions do not have a significant effect.

The findings indicate that individuals who feel that they themselves and their work “count” and “make a difference” will be more engaged in their digital labor. By clarifying the dimensionality of mattering in crowdwork and studying its differentiated effect on WE, the paper makes a contribution to research on crowdwork and the future of work. Beyond the theoretical contributions, the finding that perceived importance fosters WE has important implications for task and platform design.

The purpose of this paper is to explore the issues on how to achieve crowdsourced real-time captioning of sign language by deaf and hard-of-hearing (DHH) people, such that how a system structure should be designed, how a continuous task of sign language captioning should be divided into microtasks and how many DHH people are required to maintain a high-quality real-time captioning.

The authors first propose a system structure, including the new design of worker roles, task division and task assignment. Then, based on an implemented prototype, the authors analyze the necessary setting for achieving a crowdsourced real-time captioning of sign language, test the feasibility of the proposed system and explore its robustness and improvability through four experiments.

The results of Experiment 1 have revealed the optimal method for task division, the necessary minimum number of groups and the necessary minimum number of workers in a group. The results of Experiment 2 have verified the feasibility of the crowdsourced real-time captioning of sign language by DHH people. The results of Experiment 3 and Experiment 4 have shown the robustness and improvability of the captioning system.

Although some crowdsourcing-based systems have been developed for the captioning of voice to text, the authors intend to resolve the issues on the captioning of sign language to text, for which the existing approaches do not work well due to the unique properties of sign language. Moreover, DHH people are generally considered as the ones who receive support from others, but our proposal helps them become the ones who offer support to others.

The paper brings Foucauldian analysis of neoliberal governmentality in the discussion on the new forms of labour control within digital labour platforms. The aim of the paper is to reveal the effect of control mechanisms employed by platforms on “entrepreneurial self” within the context of work relations.

Drawing on in-depth interviews, conducted with workers under different service categories, the author undertook an extended case study of Armut.com, a digital labour platform operating in Turkey.

The study finds that competitive mechanisms employed by the platform have a considerable effect on worker self-commercialisation and self-rationalisation. This is dependent on different control mechanisms employed by the platform, based on different platform working models.

The research brings the worker subjectivities to the discussion of control within the scope of digital labour platforms. By undertaking a rare empirical study on this issue, it contributes to the theory of entrepreneurial self within the scope of work relations.

Smart-home security involves multilayered security challenges related to smart-home devices, networks, mobile applications, cloud servers and users. However, very few studies focus on smart-home users. This paper aims to fill this gap by investigating the potential interests of adult smart-home users in cybersecurity awareness training and nonfinancial rewards that may encourage them to adopt sound cybersecurity practices.

A total of 423 smart-home users between the ages of 25 and 64 completed a survey questionnaire for this study, with 224 participants from Japan and 199 from the UK.

Cultural factors considerably influence adult smart-home users’ attitudes toward cybersecurity. Specifically, cultural differences impact their willingness to participate in cybersecurity awareness training, their views on the importance of cybersecurity training for children and senior citizens and their preference for nonfinancial rewards as an incentive for good cybersecurity behavior. These results highlight the need to consider cultural differences and their potential impact when developing and implementing cybersecurity programs that target smart-home users.

This research has two main implications. First, it provides insights for information security professionals on the importance of designing cost-effective and time-efficient cybersecurity awareness training programs for smart-home users. Second, the findings may assist governments in establishing nonfinancial incentives to encourage greater uptake of cybersecurity practices among smart-home users.

The paper investigates whether adult smart-home users are willing to spend time and money to engage in cybersecurity awareness training and to encourage their children and elderly parents to participate in training, as well. In addition, the paper examines incentives, especially nonfinancial rewards, that may motivate adult smart-home users to adopt cybersecurity behaviors at home. Furthermore, the paper analyses demographic differences among smart-home users in Japan and the UK.

This paper aims to propose a method for summarizing the topics of tweets using the Wikipedia category structure as common knowledge for supplementing the understanding of the Twitter user’s interests. There are many topics in the tweets, and the topics can be treated as a tree structure. However, when the topic hierarchy is constructed using existing hierarchal clustering approach, the granularity of tweet groups differs for each user. For summarizing the topics, identification of the topics which are heterogeneous and which are not is necessary because it is easy to understand if several groups are categorized into parent groups. However, if the group units are different for each user, a number of users’ interests cannot be summarized. If some tweets are grouped into the presidential election, and the others are into Donald Trump, there cannot be a count of how many users are interested in Donald Trump.

One solution of this issue is to construct topic structures by mapping one common tree structure. In this paper, a method is proposed for constructing the topic structure using the Wikipedia category tree similar to a common tree structure. The tweets are categorized, mapped to titles of articles in the Wikipedia category tree and then visualized as the hierarchal structure to the users.

The effectiveness of the proposed hierarchal topic structure is confirmed. In theme “politics”, the proposed method works well. The main reason is that there are many technical terms about politics in the Wikipedia categories and articles. It was found that a number of the terms of politics do not have multiple meanings, multiple semantics. However, in theme “sports”, the proposed method does not perform well. The main reason for this case is that there are a number of names of people present as topic names.

One important feature of the proposed method is that it is easy to grasp not only about the topics which are heterogeneous or homogeneous with each other but also consider the missing time when extracting topics. Another feature is that the topic structures for multiple users are easy to compare with each other.

This paper argues that the informal dimensions of practice are critical for understanding workplace learning and innovation, but have been under-theorised and under-researched. This paper aims to build on the thinking of Ellström (2010), Billett (2012) and Guile (2014) to account for the emergence of innovation through practice, and propose two new concepts for improving our understanding of innovation as process: “tacit pedagogy” and “entanglement”. This argument is evidenced through a recent study of team-working in a high-profile engineering company.

Qualitative interview data was collected on the informal features of organisational culture and work processes supporting innovation, and how these features intersect and interrelate with the formal features and procedures of the organisation.

Three generic modes of team-working practice are identified which, it is suggested, are likely to be associated with innovatory working, and are observable practices available to future researchers.

Productive approaches to the organisation of work processes so as to enhance practitioner learning and the potential for innovation are evidenced and evaluated.

The concepts “tacit pedagogy” and “entanglement”, intended to improve theoretical understanding of learning and innovation through practice, are introduced.