Managing the Crowd: A Literature Review of Empirical Studies on Internal Crowdsourcing

Pohlisch, Jakob

doi:10.1007/978-3-030-52881-2_3

Cited by 6 publications

(6 citation statements)

References 44 publications

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“…Paper I. introduces a definition of internal crowdsourcing, which has been broadly adopted and confirmed by several further studies (e.g., Ulbrich, et al, 2021;Pohlisch, 2021;Iskender, et al, 2021).…”

Section: Recent Developments In Internal Crowdsourcingmentioning

confidence: 96%

“…Firms are opening up their boundaries to involve external expertise in their innovation process (Testa, et al, 2019;Pohlisch, 2019;2021;Boudreau, 2010;Boudreau et al, 2011;. Instead of just collaborating with some known externals, organizations are increasingly applying a phenomenon called "crowdsourcing" (e.g., 2021;Leimeister, et al, 2020;Pohlisch, 2021).…”

Section: Related Workmentioning

confidence: 99%

“…Although the concept of internal crowdsourcing is known for 10 years now, researchers are still talking about the "first wave of research" (Pohlisch, 2021;Benbya and Leidner 2018;Smith et al 2017;. In this section, recent developments are summarized and discussed referring to the individual paper´s research findings.…”

Section: Recent Developments In Internal Crowdsourcingmentioning

confidence: 99%

“…As highlighted especially in Paper IV., basic questions to understand internal crowdsourcing were not answered. Recent studies confirm that internal crowdsourcing requires much more theoretical consideration (Knop, et al, 2017;Pohlisch, 2021). No further research effort exists that addresses the fundamental topics prerequisites, process, and outcomes in a holistic way like presented in Paper IV.…”

Section: Author Topic Datamentioning

confidence: 99%

“…Enabled by information technology (IT), organizations are increasingly leveraging new open organizational forms to source and organize knowledge (e.g., Puranam, et al 2013;2021). Social IT is seen as a key enabler and transformer of such new processes and organizational structures, such as crowdsourcing (e.g., Baptista, et al, 2020;Schlagwein and Hu, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Understanding internal crowdsourcing

Zuchowski¹

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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.

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Section: Recent Developments In Internal Crowdsourcingmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

Section: Recent Developments In Internal Crowdsourcingmentioning

confidence: 99%

Section: Author Topic Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Understanding internal crowdsourcing

Zuchowski¹

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Inside-Out: How Internal Social Media Platforms Can Accelerate Innovation and Push External Crowdsourcing Towards New Frontiers

Rapp

Kröger²,

Scheerer³

2021

HCI in Business, Government and Organizations

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Towards Artificial Intelligence Augmenting Facilitation: AI Affordances in Macro-Task Crowdsourcing

et al. 2023

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Crowdsourcing holds great potential: macro-task crowdsourcing can, for example, contribute to work addressing climate change. Macro-task crowdsourcing aims to use the wisdom of a crowd to tackle non-trivial tasks such as wicked problems. However, macro-task crowdsourcing is labor-intensive and complex to facilitate, which limits its efficiency, effectiveness, and use. Technological advancements in artificial intelligence (AI) might overcome these limits by supporting the facilitation of crowdsourcing. However, AI’s potential for macro-task crowdsourcing facilitation needs to be better understood for this to happen. Here, we turn to affordance theory to develop this understanding. Affordances help us describe action possibilities that characterize the relationship between the facilitator and AI, within macro-task crowdsourcing. We follow a two-stage, bottom-up approach: The initial development stage is based on a structured analysis of academic literature. The subsequent validation & refinement stage includes two observed macro-task crowdsourcing initiatives and six expert interviews. From our analysis, we derive seven AI affordances that support 17 facilitation activities in macro-task crowdsourcing. We also identify specific manifestations that illustrate the affordances. Our findings increase the scholarly understanding of macro-task crowdsourcing and advance the discourse on facilitation. Further, they help practitioners identify potential ways to integrate AI into crowdsourcing facilitation. These results could improve the efficiency of facilitation activities and the effectiveness of macro-task crowdsourcing.

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Managing the Crowd: A Literature Review of Empirical Studies on Internal Crowdsourcing

Cited by 6 publications

References 44 publications

Understanding internal crowdsourcing

Understanding internal crowdsourcing

Inside-Out: How Internal Social Media Platforms Can Accelerate Innovation and Push External Crowdsourcing Towards New Frontiers

Towards Artificial Intelligence Augmenting Facilitation: AI Affordances in Macro-Task Crowdsourcing

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