Explaining Government Crowdsourcing Decisions: A Theoretical Model

Explaining Government Crowdsourcing Decisions: A Theoretical Model

Nilay Yavuz (Middle East Technical University, Turkey), Naci Karkın (Pamukkale University, Turkey) and Ecem Buse Sevinç Çubuk (Aydin Adnan Menderes University, Turkey & Delft University of Technology, The Netherlands)
DOI: 10.4018/978-1-7998-1526-6.ch008
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Crowdsourcing online has been popularly utilized especially among business organizations to achieve efficiency and effectiveness goals and to obtain a competitive advantage in the market. With the governments' increasing interest in using information and communication technologies for a variety of purposes, including generation of public value(s) and innovative practices, online crowdsourcing has also entered into the public administration domain. Accordingly, studies have investigated critical success factors for governmental crowdsourcing, or explored citizen participation in crowdsourcing activities in case studies. However, governmental decision to adopt online crowdsourcing as innovation has not been sufficiently examined in the extant literature. The objective of this chapter is to propose a theoretical model that explains the government adoption of crowdsourcing. Based on the review of case studies on governmental crowdsourcing, an integrated theoretical model of factors affecting government crowdsourcing decisions is developed.
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With the ever-growing advancements in ICTs, there emerge many opportunities for governments to innovate public service delivery, to improve democratic outcomes, and to undertake administrative reforms. One of the digitally enabled government innovations that have been gaining more attention recently is government crowdsourcing. Crowdsourcing can be defined as “the act of an organization taking a function once performed by an organization’s own employees and outsourcing it to people outside the organization (crowd) through an open call online” (Liu, 2017, p. 656; Howe, 2006a). In public context, main examples of crowdsourcing include involving citizens in the production of public services, inviting the public to solve public problems, and incorporating public participation into policy making (Nam, 2012; Liu, 2017). It is argued that crowdsourcing has the potential to provide solutions to persistent or emergent issues and problems that may not be met by traditional bureaucratic efforts (Bommert, 2010). Crowdsourcing platforms “can empower citizens, create legitimacy for the government with the people, and enhance the effectiveness of public services and goods” (Liu, 2017, p. 656). In addition, crowdsourcing can be very functional to employ the public at large for searching for new ways and methods to conduct the business, particularly at lower costs when compared to paying traditional employees (Howe, 2006b). Since crowdsourcing can be operationalized using online networks and communities through the intra- and internet, it eliminates time and space as the limiting elements together with the associated costs that public institutions are assumed to bear when aiming to address societal issues.

Nonetheless, the initial experience of governments with these online platforms has been a trial-and-error process (Brabham, 2009). While the potential benefits and challenges mentioned earlier -to some extent- explain governments’ decisions to crowdsource online, there is a need to adopt a more holistic approach in understanding governmental adoption of crowdsourcing, and consider a variety of factors in explaining this process, including the decision to use information and communication technologies.

In a study that conducts a systematic review of crowdsourcing decisions in the business sector, Thuan, Antunes and Johnstone (2016, p.48) assert that “there is lack of a commonly accepted list of factors that affect the decision to crowdsource” and “the overall picture on the crowdsourcing decision is still unveiled”. Existing research on government crowdsourcing has largely focused on dispersed case studies or the citizens’ perspective. However, factors that affect governments to engage in online crowdsourcing are not comprehensively explored in the literature.

In the light of this gap, the objective of this study is to identify factors related to government online crowdsourcing decisions. By conducting a review of the case studies on government online crowdsourcing, the chapter proposes an integrated theoretical model of factors affecting government crowdsourcing decisions, including individual perceptions, organizational factors, and environmental factors. According to this framework, some policy recommendations for government crowdsourcing decisions are made.

The paper is organized as follows. The first section discusses crowdsourcing concept and presents a theoretical framework for the study. In the next section, methodology for the study is explained, and findings from the review of the case studies on governmental online crowdsourcing are presented. Based on this review, a theoretical model is developed for government online crowdsourcing adoption. Finally, conclusions discuss research and policy implications of the model.

Key Terms in this Chapter

Crowd: Anyone who is aware of the task to be crowd sourced and who can use the Internet to contribute to such a goal.

Innovation: A new thing, method or technique.

Legitimization: Making something acceptable to the public.

Technology adoption: The behavior of accepting, owning and using a new technology.

Perceived usefulness: An individual’s belief that using a tool, a method, or a technique will bring some benefits.

Perceived ease of use: An individual’s belief that using a tool, a method, or a technique will be easy and simple.

Crowdsourcing: The activity of getting information or input for a task or a project in various sectors from a large and relatively open group of internet users.

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