Crowdsourcing as a strategic IS sourcing phenomenon: Critical review and insights for future research

Towards a typology development of crowdsourcing in science

Crowdsourcing in science as collaborative online process through which non-professional and/or professional scientists incorporate a group of individuals of varying, diversity knowledge and skills, via an open call to the Internet and/or online platforms, to undertaking of a task in science, is an important strategy to support scientific research that has gained attention in academia and practitioners. While research efforts to date have focused on the benefits of crowdsourcing in science, its typology has yet to mature. Typologies are important in describing complex, multidisciplinary organisational forms such as crowdsourcing in science. The main purpose of this article is to identify and provide a typology of crowdsourcing in science. Based on the thematic analysis of publications collected in a systematic manner and focused group interviews, 12 types of crowdsourcing in science are identified. The proposed crowdsourcing in science typology matrix may be a starting point for future research and decision-making by practitioners regarding the choice of a specific type of crowdsourcing in science.

A Data Envelopment Analysis Evaluation Study of Urban Crowd Sourcing Competitiveness Based on Evidence From 21 Chinese Cities

In the era of the global village, crowd sourcing as a new model of service outsourcing is increasingly being valued by all walks of life. This study uses the data envelopment analysis (DEA) method to explain the crowd sourcing competitiveness of service outsourcing base cities by using input-output efficiency. The crowd sourcing competitiveness among crowd sourcing base cities is organized and analyzed by collating and analyzing the data of 21 service outsourcing base cities in China from 2016 to 2019. The results show that there is no significant difference in the competitiveness of 21 service outsourcing, the overall trend is that the competitiveness of crowd sourcing is not strong, the match between input and output is not high. Comparatively speaking, Beijing, Shanghai, Nanjing, Hangzhou, Suzhou and Wuxi are more competitive in crowd sourcing. Combining with the reality of China's economic development, taking the road of crowd sourcing with Chinese characteristics is a good choice for the development of China's crowd sourcing industry.

A Fine-Grain Batching-Based Task Allocation Algorithm for Spatial Crowdsourcing

Task allocation is a critical issue of spatial crowdsourcing. Although the batching strategy performs better than the real-time matching mode, it still has the following two drawbacks: (1) Because the granularity of the batch size set obtained by batching is too coarse, it will result in poor matching accuracy. However, roughly designing the batch size for all possible delays will result in a large computational overhead. (2) Ignoring non-stationary factors will lead to a change in optimal batch size that cannot be found as soon as possible. Therefore, this paper proposes a fine-grained, batching-based task allocation algorithm (FGBTA), considering non-stationary setting. In the batch method, the algorithm first uses variable step size to allow for fine-grained exploration within the predicted value given by the multi-armed bandit (MAB) algorithm and uses the results of pseudo-matching to calculate the batch utility. Then, the batch size with higher utility is selected, and the exact maximum weight matching algorithm is used to obtain the allocation result within the batch. In order to cope with the non-stationary changes, we use the sliding window (SW) method to retain the latest batch utility and discard the historical information that is too far away, so as to finally achieve refined batching and adapt to temporal changes. In addition, we also take into account the benefits of requesters, workers, and the platform. Experiments on real data and synthetic data show that this method can accomplish the task assignment of spatial crowdsourcing effectively and can adapt to the non-stationary setting as soon as possible. This paper mainly focuses on the spatial crowdsourcing task of ride-hailing.

A TOPSIS-Inspired Ranking Method Using Constrained Crowd Opinions for Urban Planning

Crowdsourcing has become an important tool for gathering knowledge for urban planning problems. The questions posted to the crowd for urban planning problems are quite different from the traditional crowdsourcing models. Unlike the traditional crowdsourcing models, due to the constraints among the multiple components (e.g., multiple locations of facilities) in a single question and non-availability of the defined option sets, aggregating of multiple diverse opinions that satisfy the constraints as well as finding the ranking of the crowd workers becomes challenging. Moreover, owing to the presence of the conflicting nature of features, the traditional ranking methods such as the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) cannot always be feasible as the optimal solutions in terms of multiple objectives cannot occur simultaneously for the conflicting cases (e.g., benefit and cost criteria) for urban planning problems. Therefore, in this work, a multi-objective approach is proposed to produce better compromised solutions in terms of conflicting features from the general crowd. In addition, the solutions are employed to obtain a proper ideal solution for ranking the crowd. The experimental results are validated using two constrained crowd opinion datasets for real-world urban planning problems and compared with the state-of-the-art TOPSIS models.

Open and Crowd-Based Platforms: Impact on Organizational and Market Performance

The aim of the research was to present the state of the art on the use of open and crowd-based platforms and the advantages in terms of business performance that emerging practices employing such technologies are able to provide. The analysis was performed by extracting information on emerging practices from the repository Business Process Framework for Emerging Technologies developed by the Department of Industrial Engineering of the University of Salerno (Italy). Contingency tables allowed analysis of the association of such practices with industry, business function, business process, and impact on performance. From the analysis of the results, many implementation opportunities emerge, mainly in manufacturing, healthcare, and transportation industries, providing benefits not only in terms of efficiency and productivity, cost reduction, and information management but also in product/service differentiation. Therefore, the research provides an overview of opportunities for organizations employing open and crowd-based platforms in order to improve market and organizational performance. Moreover, the article highlights in what specific business contexts these technologies can be mainly useful.

The Relationship Between Persuasion Cues and Idea Adoption in Virtual Crowdsourcing Communities

Building on the elaboration likelihood model (ELM) and absorptive capacity, this study develops a four-dimensional model of idea adoption in Virtual Crowdsourcing Communities (VCCs) and examines the influence of different persuasion cues on idea adoption. The research model was tested using hierarchical logistic regression based on a dataset from the Tableau community. The results show that both community recognition of users and community recognition of ideas are positively related to idea adoption. Proactive user engagement has a significant positive impact on idea adoption, while reactive user engagement has no significant impact. Idea content quality, represented by idea length and supporting arguments, has an inverted U-shaped relationship with idea adoption. Community absorptive capacity positively moderates the curvilinear relationship between idea content quality and idea adoption. These results contribute to a better elucidation of the persuasion mechanisms underlying idea adoption in VCCs, and thus provide important implications for open innovation research and practice.

Innovation in business Intelligence systems

Innovation crowdsourcing communities play a central role for companies to advance their innovation capabilities and portfolio by leveraging crowd intelligence and knowledge. However, it remains unclear how the mechanisms and structure of innovation crowdsourcing communities affect firms' innovation performance. Based on the open innovation theory and knowledge-based view (KBV), this study develops a research model to investigate how the structure and mechanisms of innovation crowdsourcing influence firms' knowledge management and innovation performance. The model was tested using structural equation modeling based on a dataset from the Microsoft community for business intelligence tools. The results show that both organizational and technical mechanisms of the community positively influence the community structure. The community structure positively influences knowledge acquisition, knowledge transformation, and the size and diversity of crowd participation. In turn, innovation crowdsourcing mechanisms and knowledge transformation have a strong influence on innovation performance.