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Political skill has emerged as a concept of interest within the information systems literature to explain individual performance outcomes. The purpose of this paper is to adapt political skill to technology-mediated contexts. Specifically, the authors seek to understand political skill's role in shaping microtask workers' opportunity recognition when utilizing online communities in microtask work environments.

The authors tested their research model using a survey of 348 Amazon Mechanical Turk (MTurk) workers who participate in microtask-related online communities. MTurk is a large, popular microtasking platform used by thousands of microtask workers daily, with several online communities supporting microtask workers.

Technology-based political skill plays a critical role in shaping the resources microtasking workers rely upon from online communities, including opportunity recognition and knowledge sharing. The ability to develop opportunity recognition positively impacts a microtask worker's ability to leverage online communities for microtask worker performance. Tenure in the community acts as a moderator within the model.

The present study makes several contributions. First, the authors adapt political skill to an online community to account for how microtask workers understand a community's socio-technical environment. Second, the authors demonstrate the antecedent role of political skill for opportunity recognition and knowledge sharing. Third, the authors provide empirical validation of the link between online communities and microtask worker performance.

The term “crowdwork” describes a new form of digital work that is organized and regulated by internet-based platforms. This chapter examines how crowdwork platforms ensure their virtual workforce’s commitment and control its performance despite its high mobility, anonymity, and dispersion. The findings are based on a case study analysis of 15 microtask and macrotask platforms, encompassing 32 interviews with representatives of crowdwork platforms, and crowdworkers, as well as an analysis of the platforms’ homepages and community spaces. The chapter shows that performance control on crowd platforms relies on a combination of direct control, reputation systems, and community building, which have until now been studied in isolation or entirely ignored. Moreover, the findings suggest that while all three elements can be found on both microtask and macrotask platforms, their functionality and purpose differ. Overall, the findings highlight that platforms are no neutral intermediaries but organizations that adopt an active role in structuring the digital labor process and in shaping working conditions. Their managerial structures are coded and objectified into seemingly neutral technological infrastructures, whereby the underlying power relations between capital and labor become obscured.

Ensuring quality is one of the most significant challenges in microtask crowdsourcing tasks. Aggregation of the collected data from the crowd is one of the important steps to infer the correct answer, but the existing study seems to be limited to the single-step task. This study aims to look at multiple-step classification tasks and understand aggregation in such cases; hence, it is useful for assessing the classification quality.

The authors present a model to capture the information of the workflow, questions and answers for both single- and multiple-question classification tasks. They propose an adapted approach on top of the classic approach so that the model can handle tasks with several multiple-choice questions in general instead of a specific domain or any specific hierarchical classifications. They evaluate their approach with three representative tasks from existing citizen science projects in which they have the gold standard created by experts.

The results show that the approach can provide significant improvements to the overall classification accuracy. The authors’ analysis also demonstrates that all algorithms can achieve higher accuracy for the volunteer- versus paid-generated data sets for the same task. Furthermore, the authors observed interesting patterns in the relationship between the performance of different algorithms and workflow-specific factors including the number of steps and the number of available options in each step.

Due to the nature of crowdsourcing, aggregating the collected data is an important process to understand the quality of crowdsourcing results. Different inference algorithms have been studied for simple microtasks consisting of single questions with two or more answers. However, as classification tasks typically contain many questions, the proposed method can be applied to a wide range of tasks including both single- and multiple-question classification tasks.

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 purpose of this paper is to examine the efficacy of the Japanese ten-item personality inventory (TIPI-J), a short version of the big five (BF) questionnaire, on crowdsourcing. The BF traits are indicators of personality and are said to be an effective predictor of study performance in various occupations. BF can be used in crowdsourcing to predict crowd workers’ performance; however, it will be difficult to use in practice for two reasons like the time-and-effort issue and the bias issue. In this study, an empirical analysis is conducted on crowdsourcing to examine if TIPI-J can solve those issues.

To investigate the issues, two tasks are posted on a crowdsourcing provider. Both TIPI-J and full version BF are conducted before and after selecting crowd workers. Structural validity and convergence validity are tested with correlation analysis between before (TIPI-J) and after (full version BF) data to examine the bias issue. Additionally, those correlations are compared with previous study and significances are examined.

The correlations in “conscientiousness” is 0.45-0.50, respectively, compared with a previous study, those two correlations did not show significance. This indicates that no clear bias exists.

This is the first research to investigate the efficacy of TIPI-J on crowdsourcing and showed that TIPI-J can be a useful tool for predicting crowd workers’ performance and thus it can help to select appropriate crowd workers.

One potential shortcoming of the emerging gig economy is the reduced ability of gig workers to meet their social and relatedness needs through their work. Using self-determination theory (SDT) as a theoretical foundation, we examine how gig work platforms currently use their technology for managing relationships with gig workers. We develop a framework consisting of four main attributes: relationship content, relationship duration, relational eHRM function, and social eHRM type. We then analyze the eHRM resources offered on 20 gig work platforms, evaluating the extent to which they are aligned with the framework. All platforms analyzed included some relational eHRM resources. Relationship content tends to be focused on tasks or career development, and relationship duration tends to be short. All three of the relational eHRM types (communication, training and development, and performance management) are currently found on gig platforms. Many of the eHRM resources are available to an open audience, but some of the resources with the potentially highest value are kept internal and are available only to people in particular roles. We discuss practical implications and directions for future research on this topic, suggesting that eHRM systems more focused on relational functions could be used to help gig workers meet their relatedness needs.

The purpose of this paper is to explore the development of crowdsourcing literature.

This study is a comprehensive review of 346 articles on crowdsourcing. Both statistical and contents analyses were conducted in this paper.

ISI listed journal articles, non-ISI listed journal articles and conference articles have had nearly the same contribution in crowdsourcing literature. Articles published in non-ISI listed journals have had an essential role in the initial theory development related to crowdsourcing. Scholars from the USA have authored approximately the same number of articles as scholars from all the European countries combined. Scholars from developing countries have been more relatively active in authoring conference articles than journal articles. Only very recently, top-tier journals have engaged in publishing on crowdsourcing. Crowdsourcing has proven to be beneficial in many tasks, but the extant literature does not give much help to practitioners in capturing value from crowdsourcing. Despite understanding that the motivations of crowds are crucial when planning crowdsourcing activities, the various motivations in different contexts have not been explored sufficiently. A major concern has been the quality and accuracy of information that has been gathered through crowdsourcing. Crowdsourcing bears a lot of unused potential. For example, it can increase employment opportunities to low-income people in developing countries. On the other hand, more should be known of fair ways to organize crowdsourcing so that solution seekers do not get a chance to exploit individuals committing to provide solutions.

The literature included in the study is extensive, but an all-inclusive search for articles was limited to only nine selected publishers. However, in addition to the articles retrieved from the nine selected publishers, 52 highly cited articles were also included from other publishers.

Crowdsourcing has much unused potential, and the use of crowdsourcing is increasing rapidly. The study provides a thematic review of various applications of crowdsourcing.

The study is the first of its kind to explore the development of crowdsourcing literature, discussing the loci and foci of extant articles and listing applications of crowdsourcing. Successful applications of crowdsourcing include idea generation, microtasking, citizen science, public participation, wikies, open source software and citizen journalism.

Crowdsourcing platforms have emerged as an innovative way to generate ideas and solving problems. However, promoting sustained participation among crowdworkers is an ongoing challenge for most crowdsourcing platform providers. Drawing on self-determination theory, this study investigates the impacts of job autonomy on crowdworkers' sustained participation intention.

A survey of 212 crowdworkers from a leading crowdsourcing platform in China was conducted to empirically validate the model.

The empirical results lead to several key findings. First, the taxonomy of job autonomy in crowdsourcing contains three archetypes: work-scheduling autonomy, work-task autonomy, and work-method autonomy. Second, work-scheduling autonomy and work-method autonomy have more significant positive effects on temporal value than work-task autonomy, and this increase in temporal value increases crowdworkers' sustained participation intention. Third, work-task autonomy exerts a stronger influence on hedonic value than work-scheduling autonomy or work-method autonomy, and this increase in hedonic value also increases crowdworkers' sustained participation intention.

This study extends the crowdsourcing literature by examining the formation of crowdworkers' sustained participation and highlighting the role of differential effects of multidimensional job autonomy on crowdworkers' sustained participation. We believe that this study provides actionable insights into measures that promote crowdworkers' sustained participation in the crowdsourcing platform.