An organisational framework for analysis of crowdsourcing initiatives

Abstract

Purpose Since Howe (2006) first used the term crowdsourcing to define the outsourcing of activities by companies to individuals through the use of the internet, a growing literature has developed on the subject. Its emergence as a new paradigm in problem solving and value creation in the digital innovation ecosystem has produced significant publications in the management field, but publications about some aspects of organisations are still missing. Such is the case regarding the impact that these crowdsourcing initiatives have on organisations. This paper advances in that direction by proposing a new approach for the review of these activities through a detailed model of the elements of organisational levels that describe the crowdsourcing processes. The paper aims to discuss these issues. Design/methodology/approach Crowdsourcing models in the literature are reviewed to propose the new model, complemented by a survey of scholars and practitioners to validate the approach introduced. Findings The results provide a detailed link between the two fields: crowdsourcing models and organisational levels, developed through the connection of the elements of organisational levels and the dimensions of different crowdsourcing models. The identification of the elements that constitute each organisational level, their inclusion in a new model and the order of relevance of the different elements in the crowdsourcing initiatives could have an implication for academics and practitioners involved in implementing crowdsourcing. Originality/value The originality of this research is the introduction of a new approach for the analysis of crowdsourcing initiatives, establishing a new framework model to connect crowdsourcing models and organisational levels for new organisations.

Testing the theoretical principles of citizen science in monitoring stream water levels through photo-trap frames

“…The data used in this study are not included into the category of User-Generated Contents (UGC), as they were not released through social media or digital platforms (Zheng et al, 2018;See, 2019;Sy et al, 2019;Modaresnezhad et al, 2020). Rather, data was acquired using crowdsourcing methods according to precise protocols set in the research design phase of the study (Brabham, 2008;Estellés-Arolas and González-Ladrón-De-Guevara, 2012;Palacios et al, 2016;Lowry et al, 2019;Martinez-Corral et al, 2019).…”