An Aggregate Taxonomy for Crowdsourcing Platforms, their Characteristics, and Intents.
| Autor | Vianna, Fernando Ressetti Pinheiro Marques |
| Cargo | Research Article |
INTRODUCTION
According to Rieder and Vob (2010), the collective intelligence of individuals connected to electronic networks has been explored in diverse ways in a wide variety of virtual environments. A new labor model emerges, known as 'client-employee,' characterized by an attempt to involve customers in active participation in value creation, by an organization, improving the efficiency of the production process, and having them to perform as if they were employees, when in fact they are customers.
This new model of customer participation in value generation occurs primarily in virtual environments, being mediated by 'crowdsourcing platforms,' which act as 'bridges' that bring together organizations, with their problems and challenges, and unrelated people, who have skills or the creativity to address them, in a faster and less costly fashion than regular employees would (Liu & Dang, 2014). These platforms consist of ubiquitous, distributed, and innovation-enabling digital systems for the provision of services and products (Reuver, Sorensen, & Basole, 2018; Hein et al., 2020; Rolland, Mathiassen, & Rai, 2018) to users, based on the users' own efforts.
Phenomena as digital age, Industry 4.0 (Lin et al., 2018; Pilloni, 2018; Vianna, Graeml, & Peinado, 2020), and smart environments (Valerio, Passarella, & Conti, 2017; Ullo & Sinha, 2020) turned crowdsourcing platforms into essential tools for organizations, since they are the means for companies to reach data and improve problem solving, product development, and process innovations (Hartmann & Henkel, 2020; Vianna et al., 2020). Thus, trying to better understand the characteristics of these platforms and their functioning, while seeking to consolidate the elements that comprise them, represents a relevant research effort.
For Estelles-Arolas and Gonzalez-Ladron-de-Guevara (2012), the numerous possibilities of using crowdsourcing platforms, taking advantage of the crowd in the performance of tasks, have increased the complexity of the phenomenon, and made it more difficult to interpret and define such platforms' application. The diversity of denominations can be attributed to varied factors, such as particularities of the crowd, the outsourcing activity, or the social use made of the technological infrastructure. Taxonomies also vary, according to the crowdsourcing application, the role of the individuals, and tasks performed in problem solving or producing something (Saxton, Oh, & Kishore, 2013; Prpic, Taeihagh, & Melton, 2015).
Therefore, the research question that guided this work was: What is common among the various classifications and terminology used to name crowdsourcing platforms and their characteristics? Thus, the research sought to develop a taxonomy process on the types of crowdsourcing platforms. The authors aim to contribute, theoretically, to the consolidation of the understanding of classifications and characteristics of crowdsourcing platforms. The study also offers some practical results that may work as guidelines for managers in organizations on how to rely on the knowledge of connected crowds and their contribution to build the systems they need to generate value in the market.
This article presents a systematic literature review (SLR), addressing published works that discuss crowdsourcing and define terminology related to its characteristics. We sought to find the connection of the classifications attributed to crowdsourcing platforms and their characteristics, based on the cases and examples that were used by different authors to explain each type of crowdsourcing that appeared in their taxonomies or classifications. The obtained result allowed for the development of an overall taxonomy of different crowdsourcing platforms.
TAXONOMY, ONTOLOGIES, AND FOLKSONOMIES
According to Glass and Vessey (1995), with the increase of activities in a particular area, new concepts are developed, resulting in the need of new taxonomies also to be developed to organize the generated knowledge. Taxonomies and folksonomies are among the most prominent web content classification schemes (Noruzi, 2006).
The term 'folksonomy,' as a combination of 'folk' and 'taxonomy,' was introduced by Vander Wal (2004) through a post in his blog. According to him, a folksonomy is a user-generated content classification system that allows people to tag their favorite web resources with their chosen words or phrases, in natural language. According to Dotsika (2009), by combining and harnessing the distinct powers of ontology and folksonomy, web and information scientists are trying to integrate them, merging the flexibility, collaboration, and aggregation of folksonomies' information with the standardization, automated validation, and interoperability of ontologies. Table 1 shows the main differences between taxonomies, ontologies and controlled vocabulary (first column), and folksonomies and free tags (second column), according to Binzabiah and Wade (2012).
Table 1 presents a relevant distinction between a taxonomy process, which relies on systematic procedures and depends on experts for its development, and the folksonomy process, which is treated less rigorously and applied, primarily, to tag content on websites, blogs, and the like. Table 1 shows that the taxonomy process demands hierarchical structures and controlled vocabulary, while the folksonomy or free tagging process is an organic process of classification (Binzabiah & Wade, 2012). Binzabiah and Wade (2012) remind us that folksonomy tagging makes information increasingly easy to search, discover, and navigate over time. It also has the advantage of being multidimensional, as users can assign many tags to express a concept and can combine them the way they want. However, uncontrolled tagging may result in a mixture of types of things, names, genres, and formats. Thus, the taxonomy process is usually considered a more adequate process for a categorization/classification process that requires scientific rigor.
The importance of the development of taxonomies was initially perceived in the biological sciences (Nickerson, Muntermann, Varshney, & Issac, 2009). According to Bailey (1994), the study of classifications in the social sciences only started receiving more attention after the works of Max Webber and John C. McKinney, with their concepts of 'ideal type' and 'built type,' respectively. (1)
According to Simpson (1961) and Sneath and Sokal (1973), a taxonomy involves the classification and identification of different activities that are developed as the basis of a phenomenon. Dogac, Laleci, Kabak and Cingil (2002) highlight the multidimensional characteristic of a taxonomy, something that had already been pointed out by Bailey (1994), establishing a hierarchy among entities and classifications. The definition of a taxonomy in a field of research helps consolidating classifications and terminology that can be used by all those interested in it (Pitropakis, Panaousis, Giannetsos, Anastasiadis, & Loukas, 2019). For Bailey (1994) and Glass and Vessey (1995), taxonomies and classifications can refer to both processes and outcomes, with processes defining standards and outcomes being the standards themselves, when related to entities with similar characteristics.
The use of the knowledge of crowds (Surowiecki, 2005), connected through electronic networks, to improve the value proposition of enterprises has led to a contemporary phenomenon, referred to as crowdsourcing (Howe, 2006), discussed in a plethora of academic works, many of which propose taxonomies or other sorts of classifications to improve its understanding. Such phenomenon will be further explored in the next section. It still demands investigation, and presents research opportunities (Wazny, 2017), one of which is the organization of previously defined taxonomies to improve the understanding of the phenomenon and the definition of common grounds for future work.
CROWDSOURCING
The term 'crowdsourcing' was coined by Howe (2006), when discussing the possibility of engaging crowds in the performance of tasks, with Web 2.0 support. For Borromeo and Toyama (2016), crowdsourcing is a form of human computing, in which the effort of many individuals is requested to improve the quality of information or to provide a better service.
The use of crowclsourcing platforms allows for difficult problems to be solved in a much shorter time and at a reasonable cost, based on the support of many people (Hosseini, Phalp, Taylor, & Ali, 2015). Those involved receive a reward, which may be financial or intangible, for performing the necessary activities. For Quinn and Bederson (2009), platforms that monetarily reward individuals belong to a category other than those that reward efforts in a different fashion.
Even organizations with great financial power, such as those in the pharmaceutical industry, have opted to develop virtual platforms that try to engage internet users in performing activities of their interest. Innocentive is an example of platform used to propose difficult problems that challenge R&D teams in companies to specialists in the crowd (Albors, Ramos, & Hervas, 2008). The search for innovations, solutions of engineering problems, and categorization of images and tasks that require being present in specific geographic locations are all examples of situations in which companies benefit from outsourcing to crowds of internet users (Naroditskiy, Rahwan, Cebrian, & Jennings, 2012; Ranard et al., 2014).
With the advent of the digital age and phenomena such as Industry 4.0 (Lin et al., 2018; Pilloni, 2018; Vianna et al., 2020) and smart environments (Ullo & Sinha, 2020; Valerio et al., 2017), there was an increase in the use of data as an input for organizational processes (Hartmann & Henkel, 2020; Vianna et al., 2020). This data is processed through ubiquitous, distributed, and innovation-enabling digital systems that provide services and products...
Para continuar a ler
PEÇA SUA AVALIAÇÃOCOPYRIGHT GALE, Cengage Learning. All rights reserved.
