Understanding internal crowdsourcing

Faculty/Professorship: University of Bamberg  ; Fakultät Wirtschaftsinformatik und Angewandte Informatik: Abschlussarbeiten 
Author(s): Zuchowski, Oliver
Publisher Information: Bamberg : Otto-Friedrich-Universität
Year of publication: 2022
Pages: V, 177 ; Illustrationen
Supervisor(s): Fischbach, Kai  ; Weitzel, Tim  ; Fliaster, Alexander  
Language(s): English
Kumulative Dissertation, Otto-Friedrich-Universität Bamberg, 2021
DOI: 10.20378/irb-55711
Licence: Creative Commons - CC BY - Attribution 4.0 International 
URN: urn:nbn:de:bvb:473-irb-557111
This doctoral thesis is a cumulative account that summarizes the latest research about an open, social IT enabled phenomenon called "internal crowdsourcing”.

Organizations are increasingly leveraging open organizational forms to organize knowledge (Afuah and Tucci 2012; Puranam, et al. 2013; Majchrzak, et al., 2021). Social information technology (i.e., social IT) is seen an enabler and transformer of such new organizational structures (e.g., McAfee, 2009; Leonardi, et al., 2013; Schlagwein and Hu 2016; Baptista, et al., 2020). The internal use of social IT in organizations has increased substantially in recent years. In the annual McKinsey Social Media survey, over 85 percent of respondents said their companies use social IT for internal purposes (Bughin et al., 2017). To understand such new social-IT based phenomenons new theoretical considerations are required (e.g., Puranam, et al., 2014; Argote, 2012).

Inspired by the success of external crowdsourcing with customers, a number of organizations have recently adopted internal crowdsourcing with employees (e.g., BOSCH, Daimler, LEGO). Crowdsourcing refers to the practice of issuing open calls to large groups of people via social IT (Estellés-Arolas and González-Ladrón-de-Guevara, 2012). Crowdsourcing provides organizations with access to the knowledge and skills of a distributed and potentially very large “crowd” for solving a problem (Benbya and Van Alstyne, 2011; Simula and Vuori, 2012; Afuah and Tucci, 2012; Jeppesen and Lakhani, 2009; Malone et al., 2009). As an emerging phenomenon in professional praxis, it has been under-researched and many fundamental questions still remain unanswered. We know little about how internal crowdsourcing works and how it is impacting the organization (Leonardi, 2007, Majchrzak and Cherbakov, 2009; Malone, et al., 2009; Faraj, et al., 2011; 2014; Johnson, et al. 2014; Chen, 2013; Majchrzak, et al. 2000, 2021; Gilson, et al., 2014).

The purpose of this thesis is therefore to shed light on internal crowdsourcing to theorize this empirical phenomenon and to build theory about new social-IT based phenomenons in general. This cumulative thesis contains four papers that contribute to this central goal. The results of these papers have been iteratively developed, each with a distinct set of research questions. Paper I was conducted to provide a conceptual development, to synthesize the current state of literature and to provide a research agenda about internal crowdsourcing. Paper II was set up to understand the state-of-art of organizational learning theory vis-à-vis information systems in terms of drivers and barriers. The paper thus lays the foundation to apply the organizational learning lens to the empirical phenomenon internal crowdsourcing. Paper III was conducted in order to understand how organizations learn through internal crowdsourcing. The paper explains internal crowdsourcing’s learning practices. Paper IV builds on the literature on new forms of organizing and develops an organizing model focusing on internal crowdsourcing’s preconditions, nature and consequences, providing a theoretical account of internal crowdsourcing as form of organizing.
GND Keywords: Crowdsourcing; Konzeption; Forschung
Keywords: internal crowdsourcing, enterprise crowdsourcing, organisational crowdsourcing, conceptual framework, research agenda, crowdsourcing
DDC Classification: 330 Economics  
004 Computer science  
RVK Classification: ST 515   
Type: Doctoralthesis
URI: https://fis.uni-bamberg.de/handle/uniba/55711
Release Date: 4. October 2022

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