Crowdsourcing in business and management disciplines: an integrative literature review
© Hossain. 2015
Received: 27 May 2015
Accepted: 14 September 2015
Published: 18 September 2015
The objective of this study is to review crowdsourcing literature of the business and management disciplines and to know its relation with the open innovation concept. A systematic literature review is used in this study. Studies on crowdsourcing are published mostly in recent years, 2011–2013. Studies are highly dispersed, published in a very wide range of journals and are mostly based on a single case as data source. Content analysis of the findings of articles are performed to synthesize the findings in the extant literature. Most of the qualitative articles used single case method and most of the quantitative studies relied on online survey over a single crowdsourcing platform. Studies and scholars in the literature are from a limited number of countries. Although crowdsourcing as a concept overlaps with the open innovation concept, by no means, it can be considered a concept under the broad umbrella of open innovation concept. Based on identified gaps, future research avenues are presented.
KeywordsCrowdsourcing Open innovation Idea competition Idea generation Online communities User innovation
Crowdsourcing is one of several hot topics which have emerged in the last decade. The crowdsourcing concept is coined by Howe (2006) and defined as follows: “crowdsourcing represents the act of a company or institution taking a function once performed by employees and outsourcing it to an undefined (and generally large) network of people in the form of an open call”.
Crowdsourcing is an emerging topic and it has received great attention from scholars and practitioners. It overlaps with other contemporary concepts such as open innovation, collaborative innovation, and user innovation. Some scholars believe that it falls under the umbrella concept of open innovation (Ebner et al. 2009; Marjanovic et al. 2012; Wikhamn and Wikhamn 2013). However, crowdsourcing had not been considered to be a part of the open innovation concept when it was introduced by Chesbrough (2003). A study by Estellés-Arolas and González-Ladrón-de-Guevara (2012) found at least 40 definitions of crowdsourcing in the literature. What is crowdsourcing and what is not is an ongoing debate (Stieger et al. 2012). Studies on crowdsourcing are dispersed in various disciplines including business and management (B&M) disciplines.
An integrative literature review approach is used to integrate confusion and contradictory evidence that are existed in the extant literature. On the Web of Science database, for example, crowdsourcing literature is dispersed over a hundred research areas. Thus, crowdsourcing has emerged as a research topic for the scholars of many disciplines. It is used for various purposes such as collecting, mapping, and sharing data (Hudson-Smith et al. 2009), getting ideas and opinions from employees (Stieger et al. 2012), idea generation and decision making (Hossain 2012; Rosen 2011), microtasking (Alonso and Mizzaro 2012; Chandler and Kapelner 2013), and creativity (Cabiddu et al. 2013; Hossain and Kauranen 2015), among others.
Saxton et al. (2013) developed a taxonomic theory of crowdsourcing and found nine distinct forms of crowdsourcing models. Crowdsourcing has been classified in various categories. Boudreau and Lakhani (2013) classified crowdsourcing into four categories such as contests, collaborative communities, complementors, and micro tasking. Considering structural properties, nature of collaboration, and governance of various types of crowdsourcing networks, Simula and Ahola (2014) also classified crowdsourcing into four categories such as internal crowdsourcing, community crowdsourcing, open crowdsourcing, and crowdsourcing via a broker.
Zhao and Zhu (2014) contributed a review study on crowdsourcing within Information Systems discipline and identified various avenues for future studies. Many issues related with crowdsourcing are yet to be explored and reviews of crowdsourcing studies are crucial to gain comprehensive knowledge on it. How crowdsourcing literature in B&M disciplines are involving and how it is related with the open innovation concept are yet to be explored. Hence, the objective of this study is to review crowdsourcing literature of the B&M disciplines and to know its relation with the open innovation concept.
The Web of Science database is considered as the main source for articles. We used concurrently both “crowdsourcing” and “open innovation” as keywords to search articles. On the Web of Science database, articles are classified into various categories. We selected two categories – business and management – to extract articles on crowdsourcing which have been published under those two disciplines. We extracted all articles under B&M disciplines, which contain both crowdsourcing and open innovation terms. Altogether 49 articles have been found through our search on the Web of Science database. However, after reading all articles, 42 are included for analysis and other seven articles left out for their irrelevancy. To include more articles, we searched on the Scopus database and found additional seven articles. We searched on Google scholar and found one more article. Thus, 50 articles have been finally considered for this study purpose. Moreover, articles appeared in top-tier outlets on crowdfunding are additionally considered. Web of Science Core Collection is considered as the source of articles.
Based on methodologies, the articles are categorized into conceptual, qualitative, quantitative, mixed and managerial categories. A conceptual article focuses primarily on theory development and does not present data and/or analyses for the purposes of theory testing (Yadav 2010). We have categorized an article into conceptual group using the above criterion. An article is included in the qualitative group if it contains qualitative data and analysis. An article is considered as a quantitative when clear quantitative data collection process and quantitative analysis are present in that article. If an article used both quantitative and qualitative data and analysis, we included it in the category of mixed method (see Creswell 2013). An article is considered as a managerial when its focus is clearly towards practitioners even though qualitative and quantitative data and analysis, to some extent, are present in the article.
We recorded necessary information on spreadsheet. The information mainly includes names of the authors, affiliation of authors by country, years of publication, names of journals, methodologies, data, types of samples, main aims of the studies, and key findings, among others. The categories are selected based on the inspiration from an article by Short et al. (2010). The study by Short et al. (2010) categorized articles in categories such as conceptual, empirical - qualitative, empirical – quantitative to content analysis.
All articles are read and necessary contents are extracted and analyzed to understand the overall development of this field. Thus, content analysis is performed to synthesize the findings of the articles. Content analysis means ‘the objective, systematic and quantitative description of the manifest content of communication’ (Berelson 1952:18). Content analysis describes a family of analytical approaches ranging from intuitive, interpretive analyses to systematic, strict textual analysis (Rosengren 1981).
To find how crowdsourcing as a concept is related with the open innovation concept, we recorded if the open innovation as a term is used in the title, abstract and list of keywords. If the open innovation concept is mentioned anywhere of the above places, we considered that in those articles crowdsourcing concept is perceived as highly related concept with the open innovation concept. On the other hand, if open innovation is only mentioned somewhere in the main body of an article, we considered low relation of crowdsourcing with the open innovation concept. If open innovation is not mentioned anywhere of an entire article, we considered no relation between these two concepts.
The trends in crowdsourcing literature
List of journals and their numbers of published articles
Harvard Business Review
International Journal of Research in Marketing
International Journal of Knowledge Management
Academy of Management Review
International Journal of Research in Marketing
Creativity and Innovation Management
Decision Support Systems
Journal of Management Information Systems
Journal of Marketing
Technological Forecasting and Social Change
Journal of Product Innovation Management
Academy of Management Perspectives
Journal of Technology Management & Innovation
Business Information Review
Long Range Planning
California Management Review
Computers, Environment and Urban Systems
Management Research Review
Expert Systems with Applications
Industrial Marketing Management
Information Systems and e-Business Management
MIS Quarterly Executive
Innovation: Management, Policy & Practice
MIT Sloan Management Review
International Journal of Electronic Commerce
International Journal of Innovation Management
Science and Public Policy
International Journal of Knowledge Management
The Innovation Journal: The Public Sector Innovation Journal
Proximity of crowdsourcing to open innovation concept
“Open innovation” mentioned at
Title, abstract, or list of keywords of an article
Main body of an article
Content analysis of all 50 articles is conducted to understand the overall portrait of literature. Conceptual articles are analyzed to understand the overall development of various concepts followed by empirical articles (qualitative, quantitative, and mixed). The articles with managerial focus are analyzed to explore contributions of articles specifically for practitioners even though all articles may contain managerial discussions to some extent.
Summary of the conceptual studies
Afuah and Tucci (2012)
Under certain situations crowdsourcing transforms distant search into local search, improving the efficiency and effectiveness of problem solving.
Avenali et al. (2013)
The value of the open contract based challenge mechanism lies in its usefulness as a tool to promote collective innovation by supporting IPR management and knowledge negotiation.
Crowdsourcing is good for problem solving but value capturing is missing.
Bogers and West (2012)
Contrast the predictions of perspectives on the sources, motivation, and value appropriation of external innovation; propose a framework for the strategic management of distributed innovation.
Garrigos-Simon, et al. (2012)
Possible of competitive advantages open to organizations in terms of recent innovations, and highlights the development that need to implement to improve decision management process and exploit new situations.
Lampel et al. (2012)
The evolution of design competitions and highlights their expanding scope and complexity.
The value of Marketing Scholarships 2.0
Marjanovic et al. (2012)
Who is crowdsourcing and how. Diversity, core features and variables of various crowdsourcing models.
Olson and Rosacker (2013)
Find which researchers and organizations can leverage crowdsourcing and open source software.
Wikhamn and Wikhamn (2013)
Find how various concepts such as toolkits, innovation contests, crowdsourcing, and innovation intermediaries under the umbrella of open innovation are interrelated.
Bogers and West (2012) provided a framework for the strategic management of distribution innovation. Garrigos-Simon et al. (2012) demonstrated the impact of social networks and Web 3.0 technology to improve competitive advantage of organizations. Conceptualizing the open innovation for design competitions, Lampel et al. (2012) examined the architecture and governance of design competitions and explore how open innovation and crowdsourcing transforms design competitions. Rationale for Marketing Scholarship 2.0 – a more digital, collaborative approach for marketing knowledge production is getting significant (Lutz 2011).
Some scholars consider crowdsourcing as an under-researched type of open innovation (Marjanovic et al. 2012). Researchers and organizations can leverage from crowdsourcing and open source software concepts (Olson and Rosacker 2013). Wikhamn and Wikhamn (2013) introduced an integrated framework for open innovation and showed how, under the umbrella of open innovation, various concepts such as toolkits, innovation contests, crowdsourcing, and innovation intermediaries are interrelated. Thus, we have found that several conceptual articles have considered crowdsourcing as a part of the open innovation concept.
Summary of the qualitative studies
Battistella and Nonino (2012)
Design of motivational system needs to consider the different stages of the innovation process. Open innovation platforms moving from work place logic to social place logic is necessary.
Bojin et al. (2011)
A case platform
Design of a crowdsourcing model even for small collection of individuals is proven to be feasible.
Chanal and Caron-Fasan (2010)
A case platform
The open business model on online communities requires a multi-level incentive model.
Cummings et al. (2013)
A case platform
Benefits of crowdsourcing for R&D problems rather than solutions were identified, including generating a potential pipeline of projects and clients as well as avoiding the challenge to the professional status of the organization’s research capability.
Djelassi and Decoopman (2013)
Propose a model to show the interrelations between different components of a business model and the interactions between firms and their customers. Crowdsourcing benefits both firms and their customers.
Franzoni and Sauermann (2014)
Find the heterogeneity concerning what crowd science projects deal and how they are organized. Identifies two fundamental elements that characterize crowd science projects – open participation and open sharing of intermediate inputs.
Lampe et al. (2011)
A case platform
Social media projects designed to interact with the public can be initiated with reasonable expectations. Implementation of such projects is difficult due to limitations imposed namely by social, technical and task dependencies.
Mortara et al. (2013)
The success of idea competitions as an acquisition mechanism remains uncertain because their output is often lower compared to the required input.
Schweitzer et al. (2012)
218 ideas from an idea competition and 52 ideas from focus groups
Idea competitions lead to more and better ideas at a lower cost while focus groups yield richer interactions with users.
Weeks and Veltri (2013)
A case platform
Highlights elements that enhance and impair knowledge creation in a voluntary crowdsourcing community.
Cummings et al. (2013) suggest that crowdsourcing is a new strategic possibility for research and development (R&D) organizations to complement their internal competencies. Djelassi and Decoopman (2013) found that the crowdsourcing affects the elements of existing business models and marketing functions of firms. The authors believed that crowdsourcing offers benefits for both firms and customers. It is apparent that crowdsourcing is useful for firms in many ways.
Franzoni and Sauermann (2014) highlighted the heterogeneity of crowd in crowd science and showed how crowd science is different from other knowledge production regimes such as innovation contest and traditional science. The value of social media to engage with various stakeholders such as policy makers, government officials, and residents for the greater good of a society is significant (Lampe et al. 2011).
The success of idea competitions as a mechanism for acquiring ideas is challenging because very few ideas are selected from a huge pool of ideas submitted on crowdsourcing platforms (Mortara et al. 2013). However, idea competition brings some benefits such as improved intelligence and public relations. Comparing 218 ideas from an idea competition with 52 ideas from focus groups, Schweitzer et al. (2012) found that idea competition yields more ideas with lower cost per idea than focus groups which yield, however, richer interaction with users.
Idea competition is a valuable option to generate ideas with limited costs (Weeks and Veltri 2013). However, idea screening to find promising ideas from a large pool of ideas is a major barrier for organization to use idea competition. It seems that idea competition and information sharing on online forums are increasingly growing as parts of crowdsourcing.
Summary of the studies based on quantitative method
348 implemented ideas of 8801 submitted ideas by 4285 individuals
Only few of abundant submitted ideas are implemented and most of the implemented ideas are proposed by serial ideators whereas new ideators are less successful to become serial ideators.
Castillo et al. (2013)
780 clusters of microblog messages
There are measurable differences in the way microblog message propagates. These differences are related to newsworthiness and credibility of the information conveyed. Features of messages makes effective for of information veracity.
Costa et al. (2013)
600 000 web comments
Propose a framework to demonstrate experimentally that non-experts classification performed by crowds to define user profile.
Crooks and Wise (2013)
GIS data of Haiti earthquake
The proposed model can potentially provide a link between socio-cultural information about the people affected and human relief organizations.
Dalal et al. (2011)
Data from an online survey on a platform
Provides conceptually why the proposed model is an appropriate model for eliciting expert opinions, and illustrates main components by using an infrastructure investment.
Ebner et al. (2009)
A single case
The search and identification of topics of an idea competition need to be broad to attract numerous participants. The incentive structure needs to be attractive for the participants and appropriate for firms.
Franke et al. (2013)
External contributors to firm innovation are not exclusively driven by fairness considerations. Expectations of distributive and procedural fairness impact rules underlying a crowdsourcing system.
Frey et al. (2011)
104 samples from an online survey on a platform
Monetary incentive tends to be positively related to making non-substantial contribution, whereas non-monetary incentive tends to breed more substantial postings.
Ghose et al. (2012)
8099 samples from online sources
Highlights how social media can be mined and incorporated in a model to generate a hotel ranking system based on average utility gain a consumer receives from a particular hotel.
Karvetski et al. (2013)
Data from two events
Establish a model that can be used to remedy that occurs when forecasters use the probability that represents epistemic uncertainty.
Leimeister et al. (2009)
32 samples from an online survey on a platform
Find optimal incentive and motivation for an online idea competition.
Levine and Prietula (2014)
Various agents of a collaboration innovation
Open collaboration performs well even in harsh environments including cooperators are a minority, free riders are present, and lacking of diversity.
Muhdi and Boutellier (2011)
117 samples from online survey of two platforms
Find 16 significant differences between the perceptions of members of the two investigated innovation communities.
Poetz and Schreier (2012)
Experiment data of 51 professional and 52 user ideas
User generated ideas score significantly higher in novelty and customer benefit, and somewhat lower in feasibility.
91 testing races covering 770 testing cases.
A race history maximizes system accuracy and payout. An informational inequality exists within the harness racing market
Shao et al. (2012)
2524 projects of a crowdsourcing platform
Higher awards, easier tasks, longer duration and lower competition intensity lead to a higher number of solvers. Higher awards, longer duration and higher difficulty level of tasks lead to higher ability level of winners.
Sun et al. (2012)
205 samples of an online survey from a platform
Negative interaction effect between extrinsic motivation and task complexity, and positive interaction effect between intrinsic motivation and self-efficacy.
Zheng et al. (2011)
283 samples of an online survey from a platform
Intrinsic motivation is more important than extrinsic motivation to induce participation. Contest autonomy, variety, and analyzability are positively associated with intrinsic motivation, whereas contest tacitness was negatively associated with intrinsic motivation.
Extracting data of Haiti earthquake, Crooks and Wise (2013) proposed a model that can potentially help to link socio-cultural information about the people affected with relevant humanitarian relief organizations whereas Dalal et al. (2011) proposed a model for eliciting expert opinions. Ebner et al. (2009) presented an integrated concept of online idea competitions for leveraging the potential of crowds in a real-world setting.
Based on two experimental simulations, Franke et al. (2013) found that fairness expectations with regard to the distribution of value between firms and contributors impact the likelihood of participation of crowd in idea generation activities beyond the considerations of self-interest. Monetary rewards are positively related to non-substantial contributions (Frey et al. 2011). In turn, non-monetary rewards lead to more substantial postings. Using machine learning techniques, Ghose et al. (2012) proposed a random coefficient hybrid structural model for hotel ranking considering user behavior on social media and search engines. Based on two experiments over a crowdsourcing platform, a study by Karvetski et al. (2013) argued that multiple related individual forecasts can be useful to improve aggregation of probabilities.
Successful implementation and maintenance of idea competition is important for the development of promising ideas (Leimeister et al. 2009). Levine and Prietula (2014) found that open collaboration is useful even in unforgiving environments: when cooperators are a minority, free riders are present, diversity is low, and goods are rival. Muhdi and Boutellier (2011) compared between an online Swiss innovation intermediary community and an off-line internal innovation community. They found that both monetary and non-monetary rewards are top motivators in the intermediary community whereas options such as Webcam, chat function, connection with other social networks platform are low motivators in both communities. A study by Poetz and Schreier (2012) compared ideas generated by a firm’s professionals with those generated by users; ideas generated by users score significantly higher in novelty and customer benefit, and somewhat lower in feasibility than those generated by professionals.
Schumaker (2013) developed a prediction system based on machine learning techniques. The author found that within the domain of harness racing, the prediction system outperforms crowds and other existing systems. Exploring an idea competition platform in China, Shao et al. (2012) found that higher reward, longer duration and lower intensity of competition lead to engage higher number of solvers; in contrary, higher reward, longer duration, and higher level of task difficulty result in higher level of winners’ ability.
Both extrinsic and intrinsic motivations have significant influence for sustained participation intention (Sun et al. 2012). A negative interaction affects extrinsic motivation whereas positive interaction affects intrinsic motivation and both negative interaction and positive interaction affect task complexity. Zheng et al. (2011) argue that intrinsic motivation is more important than extrinsic motivation to induce crowds for participation. They believed that a balance of both extrinsic and intrinsic motivation is necessary to encourage participation in crowdsourcing.
They also found that contest autonomy variety and analyzability are positively associated with intrinsic motivation, whereas contest tacitness is negatively associated with intrinsic motivation.
Studies based on mixed method
Summary of the studies based on mixed method
Agerfalk and Fitzgerald (2008)
13 interviews & 207 response from an online survey
Openness, trust, tact, professionalism, transparency, and complementariness are the keys factors to build an overall open sourcing ecosystem.
Battistella and Nonio (2013)
Champion and expert roles are attracted to that support knowledge acquisition, sharing or creation. Relationship roles are attracted as part of community. Process roles are attracted to OIPs that support innovation process.
Hutter et al. (2011)
541 ideas from an idea competition
The firm-level concept of co-opetition may be relevant for an innovation’s success on the individual level with contest communities.
Nishikawa et al. (2013)
Interviews and secondary data for network analysis
User-generated products generally contain higher novelty, outperformed their designer-generated counterparts on key market performance metrics.
Hutter et al. (2011) explored a community based design competition using the data from the OSRAM LED design contest which took place in 2009. The data of this study included over 1890 evaluations and 3285 comments made by participants. Qualitative part includes content analysis of the context of behavior in the community. Quantitative analysis was based on social network analysis. Subsequently, both qualitative and quantitative analyses are combined to verify, confirm and refine their findings. The authors found that the firm-level concept of co-opetition can be pertinent for the success of an innovation in the individual level of contest communities. Analyzing six user-generated products with 37 designer-generated products of Muji, Nishikawa et al. (2013) found that user-generated products have higher novelty than designer-generated products. Furthermore, on some key market performance metrics, user-generated products outperformed designer-generated products.
Summary of the studies with managerial focus
Blohm et al. (2013)
14 expert interviews
Identifies the challenges of implementing crowdsourcing and shows how organizational leaders can build the necessary absorptive capacity to capture value from crowdsourced data.
Companies face far more challenges to search ideas outside of the traditional places. Although success looks simple, a complex underlying mechanism is required for harnessing the power of collective intelligence.
Boudreau and Lakhani (2013)
Excluding crowdsourcing from corporate innovation strategy means losing opportunity. Managers resist crowdsourcing mainly because they are not clear enough what kinds of problems a crowd really can solve and how to manage the crowdsourcing process.
Healy and Ramanna (2013)
Corrupt practice is less defensible in developing countries for multinationals in overseas. Citizen-driven initiative mitigates corruption.
An idea competition
Harnessing wisdom of crowds needs much more than simply developing an online platform and putting up reward. It involves sheer labor and complexity.
Kaikati and Kaikati (2013)
Provides guidelines for negotiating cashless marketplace exchange in some domains such as crowdsourcing, promotions, and word of mouth.
Lauto et al. (2013)
An idea competition
An idea competition process that asking participants to comments on proposals improves idea generation and screening and that active involvement of R&D managers in competition process is a key factor in idea competition.
By setting forth a credible but challenging goal with minimal hurdles to entry, prizes rally diverse approaches around a focused agenda, supporting an entire field of innovators rather than a single solution.
Boudreau and Lakhani (2013) showed that despite numerous success cases, only handful of firms are capable to use crowdsourcing effectively. They found that managers are typically reluctant to invite external people to solve internal problems and tend to avoid intellectual property dilemma involved in this process. To get fuller advantages from crowdsourcing, the authors urge firms to consider four approaches such as contests, collaborative communities, complementors, and labor market. Crowd fights against corruption, which business leaders no longer can ignore. Citing various cases from countries such as China, India, and Turkey along with an in-depth analysis of a movement called RosPil in Russia, Healy and Ramanna (2013) illustrated that the movements of crowds against corruption can benefit western firms to capture value from emerging countries.
From a hands-on experience, Jouret (2009) discovered that crowdsourcing is not so simple rather it requires much more than simply developing an online platform and putting up reward. Running an idea competition requires sheer labor and involves high complexity. Kaikati and Kaikati (2013) demonstrated cashless crowdsourcing models. They claimed that examples such as Wikipedia, Threadless – an online t-shirt selling firm, and Txteagle – cell phone for micro tasking from emerging countries are well known examples of cashless crowdsourcing. Lauto et al. (2013) argued how online collaboration is a powerful means for idea generation in large firms. Based on an idea competition, they found that asking participants to comments on ideas improve idea generation and screening. They suggest that active involvement of R&D management is a key success factor in an idea competition. Organizations can use competitions to enhance R&D portfolio and engage user communities by embracing diverse risk-takers and investors (Wagner 2011).
Crowdfunding: a new emergent in crowdsourcing
From the above discussion we found that crowdsourcing is mainly used for creative crowdsourcing, microtasking, wisdom of the crowd, and idea contest. Recently, however, crowdsourcing has expanded to a new dimension called crowdfunding. In crowdfunding, crowds provide financial support to a project which needs funding to scale up. Articles on crowdfunding on top-tier journals started appearing from the beginning of 2014. According to Crowdfunding Industry Report (2015), crowdfunding can mainly be of the following categories: Donation based crowdfunding, equity based crowdfunding, lending based crowdfunding, reward based crowdfunding, and royalty based crowdfunding. Moreover, hybrid-based crowdfunding is also a considerable type of crowdfunding. The report also found that crowdfunding industry raised $16.2 billion in 2014 as funding which is a 167 % increase over the $6.1 billion raised in 2013 and the share of lending based crowdfunding is two-thirds (69 %) of the total fundraising. Crowdfunding is considered as a most recent research stream of the broad crowdsourcing discipline. Several journals have published special issues on crowdfunding. Venture Capital journal published the earliest special issue in 2013 followed by journals such as Strategic Change, New Media & Society and Entrepreneurship Theory and Practice in 2015. Thus, crowdfunding has received great attention of scholars, practitioners and policy makers. The empirical evidences of contributory studies (which are not many) on crowdfunding are summarized below. Studies indicate various research themes that need to consider in future research .
A study by Schwienbacher and Larralde (2012) demonstrated that the importance of crowdfunding as an alternative and viable source of funding is growing enormously. Burtch et al. (2013) empirically examined social influence in online journalism crowdfunding projects. They found two crucial results: (1) funders may experience a decrease in their marginal utility from their contributions and (2) the degree of exposure that a pitch receives during the funding process is positively related with readership of the story’s publication. Mollick (2014) found that personal network and project quality are two pivotal issues for successful crowdfunding efforts. Moreover, he also found that project founders strive to fulfill their obligations to funders but overfunded projects are particularly vulnerable to delay in fulfilling obligations. Comparing between pre-ordering (reward) and profit sharing (equity) crowdfunding, Belleflamme et al. (2014) derived that profit sharing is optimal for entrepreneurs with large capital requirements; along with early capital raising, pre-ordering scheme helps founders to know the market potential of their products.
Agrawal et al. (2015) examined a crowdfunding platform that connects artists with funders. They found that despite the role of the Internet to reduce numerous distance-related frictions, local and distant funders possess different funding patterns. Local funders are less responsive to the figure of cumulative funds raised by an artist. Mollick and Nanda (2015) also studied an art project to understand how crowd differs from experts in judging which project to fund. They found that there are significant agreement in funding decision of crowds and experts. Thus, their findings suggest that crowds can be used as a complementary to experts for funding decision.
Using social capital theory and collecting data from China and the USA, Zheng et al. (2011) compared entrepreneurs’ social network impact on crowdfunding. They found that entrepreneurs’ social network ties, obligations to fund other entrepreneurs, and the common meaning of the crowdfunding project between an entrepreneur and sponsors have significant effects on crowdfunding performance in China and the USA. However, the predictive power of social capital is stronger in China than that in the USA and obligation has greater impact in China. Colombo et al. (2015) argue that the effect of internal social capital on the success of a campaign is fully mediated by the capital and backers collected in the campaign’s early days. However, Ahlers et al. (2015) found that in equity crowdfunding both social capital and intellectual capital have little or no impact on funding success.
Using cognitive evaluation theory in micro-lending crowdfunding, Allison et al. (2015) revealed that lenders react positively to the narratives that highlights the venture as an opportunity to help others and less positive when the narrates are framed as a business opportunity. In the same vein, Belleflamme et al. (2013) argue that nonprofit organizations tend to be significantly more successful than for-profit organization in achieving their fundraising targets. A study by Cummings et al. (2013) explored equity crowdfunding in the context of Canada when equity crowdfunding was not in practice. It found that potential funders’ perceived motivations for equity crowdfunding include finance, non-finance, diversification, networking, support, etc. However, the study indicates that non-financial motives have limited role in equity crowdfunding. In the same vein, Cholakova and Clarysse (2015) found that non-financial motives play no significant role in equity crowdfunding projects.
Studies on crowdfunding is still in nascent stage. Some conflicting results especially on venture quality such as human capital, social capital, and intellectual capital have appeared in the current studies. Crowdfunding as a research field and practice is evolving, institutional reforms are taking place and new (regulatory) challenges are emerging (Harrison 2013). However, one thing is clear that crowdfunding started as a promising field of research. It is expected to move from its current marginal stage to the mainstream research field.
To adopt crowdsourcing as an activity, firms need to change their business models and strategies. However, how firms change their business models and strategies over a period of time is sparse. Although much research on crowdsourcing is already accomplished, optimum mechanisms of various types of crowdsourcing are still limitedly known.
The evolution of crowdsourcing is documented in a huge pool of literature, which can be compiled together succinctly to understand the overall development of this field. The degree of necessary engagement and roles of top management throughout a crowdsourcing process are crucial but their roles in this regard are not well rounded.
Incentives to motivate participants to engage in crowdsourcing platforms are essential. What kinds of incentive trigger crowds to participate in a particular context is crucial to explore. Motivation depends on social context, education, age, and culture along with many other traits. How a particular crowdsourcing platform can structure its incentive to get best value is a challenging issue. Some individuals are motivated by monetary incentives whereas others are by non-monetary incentives. So, how to design an appropriate motivation structure to attract both kinds of individuals needs rigorous investigation.
Even though crowdsourcing has been proven as a promising choice for idea generation, its potential to capture value still remains under a great debate. Crowds offer ideas that they believe to have better solution based on their experiences whereas professionals of a firm generate ideas which should compile with resources and business strategy of firms. One popular manifestation of crowdsourcing is idea competition. However, when should firms go for idea competition and for what kind of problems (complex to simple) is an unanswered question in the literature.
How firms can trade-off between internal and external sources for innovation is a standing impasse for managers. Hence, motives of these two groups differ significantly. Professionals work under firms’ management whereas crowds work staying outside the scope of firms’ control boundary. Thus, traditional human resources frameworks are not applicable for crowd management. It is also necessary to explore implicit benefits of crowdsourcing such as marketing, public relation, intelligence, building long-term relation with participants, and probably most importantly shifting locus of cost of ideation from firms to crowds. Monetary incentives might be more relevant to attract crowds from low income regions. Insight into the optimum structures and processes to identify single or several best ideas from a large number of ideas is necessary. The knowledge regarding the degree of benefits for contributors in different types of crowdsourcing platforms is not well-grounded in the literature. Crowdsourcing in B&M disciplines is still in a nascent stage. Scholars need to engage actively to enrich this field.
Studies on crowdsourcing proliferated into various directions such as idea generation, microtasking, open source software, public participation, citizen science, citizen journalism, and wikies. However, crowdfunding is a more recently emerged research stream and it is appearing to become as an independent and mainstream research discipline.
This study provides an integrative review of the extant literature on crowdsourcing in business and management disciplines. Moreover, it established a relationship between crowdsourcing and open innovation concepts. Most of the articles on crowdsourcing have been published in recent several years. It is found that articles on crowdsourcing appeared in wide range of outlets. Content analysis of the reviewed articles showed that qualitative studies are mostly based on single case study whereas the quantitative studies are based mainly on online survey over a single crowdsourcing platform. Although crowdsourcing as a concept overlaps with open innovation but it can be considered a concept under the broad umbrella of open innovation concept.
The author is grateful to the Finnish Cultural Foundation for funding this study.
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- Afuah, A, & Tucci, CL. (2012). Crowdsourcing as a solution to distant search. Academy of Management Review, 37(3), 355–375.View ArticleGoogle Scholar
- Afuah, A, & Tucci, CL. (2013). Crowdsourcing: useful for problem solving, but what about value capture? Academy of Management Review, 38(3), 457–460.View ArticleGoogle Scholar
- Agerfalk, PJ, & Fitzgerald, B. (2008). Outsourcing to an unknown workforce: exploring opensourcing as a global sourcing strategy. MIS Quarterly, 32(2), 385–409.Google Scholar
- Agrawal, AK, Catalini, C, & Goldfarb, A. (2015). The geography of crowdfunding. Journal of Economics and Management Strategy (Special Issue: Innovation Economics), 24(2), 253–274.View ArticleGoogle Scholar
- Ahlers, GK, Cumming, D, Günther, C, & Schweizer, D. (2015). Signaling in equity crowdfunding. Entrepreneurship: Theory and Practice, 39(4), 955–980.Google Scholar
- Allison, TH, Davis, BC, Short, JC, & Webb, JW. (2015). Crowdfunding in a prosocial microlending environment: examining the role of intrinsic versus extrinsic cues. Entrepreneurship: Theory and Practice, 39(1), 53–73.Google Scholar
- Alonso, O, & Mizzaro, S. (2012). Using crowdsourcing for TREC relevance assessment. Information Processing and Management, 48(6), 1053–1066.View ArticleGoogle Scholar
- Avenali, A, Battistella, C, Matteucci, G, & Nonino, F. (2013). A mechanism for supporting collective innovation: the open contract-based challenge. Information Systems and e-Business Management, 11, 541–568.View ArticleGoogle Scholar
- Battistella, C, & Nonino, F. (2012). Open innovation web-based platforms: the impact of different forms of motivation on collaboration. Innovation: Management, Policy and Practice, 14(4), 557–575.Google Scholar
- Battistella, C, & Nonino, F. (2013). Exploring the impact of motivations on the attraction of innovation roles in open innovation web-based platforms. Production Planning and Control: The Management of Operations, 24(2–3), 226–245.View ArticleGoogle Scholar
- Bayus, BL. (2013). Crowdsourcing new product ideas over time: an analysis of the Dell IdeaStorm community. Management Science, 59(1), 226–244.View ArticleGoogle Scholar
- Belleflamme, P, Lambert, T, & Schwienbacher, A. (2013). Individual crowdfunding practices. Venture Capital, 15(4), 313–333.View ArticleGoogle Scholar
- Belleflamme, P, Lambert, T, & Schwienbacher, A. (2014). Crowdfunding: tapping the right crowd. Journal of Business Venturing, 29(5), 585–609.View ArticleGoogle Scholar
- Berelson, B. (1952). Content analysis in communication research. Glencoe, Illinois: The Free Press.Google Scholar
- Blohm, I, Leimeister, JM, & Krcmar, H. (2013). Crowdsourcing: how to benefit from (too) many great ideas. MIS Quarterly Executive, 12(4), 199–211.Google Scholar
- Bloodgood, J. (2013). Crowdsourcing: useful for problem solving, but what about value capture? Academy of Management Review, 38(3), 455–457.View ArticleGoogle Scholar
- Bogers, M, & West, J. (2012). Managing distributed innovation: strategic utilization of open and user innovation. Creativity and Innovation Management, 21(1), 61–75.View ArticleGoogle Scholar
- Bojin, N, Shaw, CD, & Toner, M. (2011). Designing and deploying a ‘compact’crowdsourcing infrastructure: a case study. Business Information Review, 28(1), 41–48.View ArticleGoogle Scholar
- Bonabeau, E. (2009). Decisions 2.0: the power of collective intelligence. MIT Sloan Management Review, 50(2), 45–52.Google Scholar
- Boudreau, KJ, & Lakhani, KR. (2013). Using the crowd as an innovation partner. Harvard Business Review, 91(4), 60–69.Google Scholar
- Burtch, G, Ghose, A, & Wattal, S. (2013). An empirical examination of the antecedents and consequences of contribution patterns in crowd-funded markets. Information Systems Research, 24(3), 499–519.View ArticleGoogle Scholar
- Cabiddu, F, Castriotta, M, Di Guardo, MC, & Floreddu, P. (2013). Open innovation and crowdsourcing communities design: a cross case analysis. Designing Organizational Systems, 1, 143–155.View ArticleGoogle Scholar
- Castillo, C, Mendoza, M, & Poblete, B. (2013). Predicting information credibility in time-sensitive social media. Internet Research, 23(5), 560–588.View ArticleGoogle Scholar
- Chanal, V, & Caron-Fasan, ML. (2010). The difficulties involved in developing business models open to innovatio§n communities: the case of a crowdsourcing platform. Management, 13(4), 318–340.Google Scholar
- Chandler, D, & Kapelner, A. (2013). Breaking monotony with meaning: motivation in crowdsourcing markets. Journal of Economic Behavior & Organization, 90, 123–133.View ArticleGoogle Scholar
- Chesbrough, HW. (2003). Open innovation: the new imperative for creating and profiting from technology. Cambridge, MA: Harvard Business Press.Google Scholar
- Cholakova, M, & Clarysse, B. (2015). Does the possibility to make equity investments in crowdfunding projects crowd out reward‐based investments? Entrepreneurship Theory and Practice, 39(1), 145–172.View ArticleGoogle Scholar
- Colombo, MG, Franzoni, C, & Rossi‐Lamastra, C. (2015). Internal social capital and the attraction of early contributions in crowdfunding. Entrepreneurship: Theory and Practice, 39(1), 75–100.Google Scholar
- Costa, J, Silva, C, Antunes, M, & Ribeiro, B. (2013). Customized crowds and active learning to improve classification. Expert Systems with Applications, 40(18), 7212–7219.View ArticleGoogle Scholar
- Creswell, JW (2013). Research design: qualitative, quantitative, and mixed methods approaches. London, United Kingdom: Sage.Google Scholar
- Crooks, AT, & Wise, S. (2013). GIS and agent-based models for humanitarian assistance. Computers, Environment and Urban Systems, 41, 100–111.View ArticleGoogle Scholar
- Crowdfunding Industry Report. (2015). 2015CF – Crowdfunding industry report. Available at http://reports.crowdsourcing.org/index.php?route=product/product&product_id=54&tracking=5510b714cd9f5. Accessed on December 11, 2014.
- Cummings, S, Daellenbach, U, Davenport, S, & Campbell, C. (2013). “Problem-sourcing”: a re-framing of open innovation for R&D organisations. Management Research Review, 36(10), 955–974.View ArticleGoogle Scholar
- Dalal, S, Khodyakov, D, Srinivasan, R, Straus, S, & Adams, J. (2011). ExpertLens: a system for eliciting opinions from a large pool of non-collocated experts with diverse knowledge. Technological Forecasting and Social Change, 78(8), 1426–1444.View ArticleGoogle Scholar
- Djelassi, S, & Decoopman, I. (2013). Customers’ participation in product development through crowdsourcing: issues and implications. Industrial Marketing Management, 42(5), 683–692.View ArticleGoogle Scholar
- Ebner, W, Leimeister, JM, & Krcmar, H. (2009). Community engineering for innovations: the ideas competition as a method to nurture a virtual community for innovations. R&D Management, 39(4), 342–356.View ArticleGoogle Scholar
- Estellés-Arolas, E, & González-Ladrón-de-Guevara, F. (2012). Towards an integrated crowdsourcing definition. Journal of Information Science, 38(2), 189–200.View ArticleGoogle Scholar
- Franke, N, Keinz, P, & Klausberger, K. (2013). “Does this sound like a fair deal?” Antecedents and consequences of fairness expectations in the individual’s decision to participate in firm innovation. Organization Science, 24(5), 1495–1516.View ArticleGoogle Scholar
- Franzoni, C, & Sauermann, H. (2014). Crowd science: the organization of scientific research in open collaborative projects. Research Policy, 40(1), 105–122.Google Scholar
- Frey, K, Lüthje, C, & Haag, S. (2011). Whom should firms attract to open innovation platforms? The role of knowledge diversity and motivation. Long Range Planning, 44(5), 397–420.View ArticleGoogle Scholar
- Garrigos-Simon, FJ, Alcamí, RL, & Ribera, TB. (2012). Social networks and Web 3.0: their impact on the management and marketing of organizations. Management Decision, 50(10), 1880–1890.View ArticleGoogle Scholar
- Ghose, A, Ipeirotis, PG, & Li, B. (2012). Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Science, 31(3), 493–520.View ArticleGoogle Scholar
- Harrison, R. (2013). Crowdfunding and the revitalisation of the early stage risk capital market: catalyst or chimera? Venture Capital, 15(4), 283–287.View ArticleGoogle Scholar
- Healy, PM, & Ramanna, K. (2013). When the crowd fights corruption. Harvard Business Review, 91(1/2), 122–129.Google Scholar
- Hossain, M. (2012). Performance and potential of open innovation intermediaries. Procedia-Social and Behavioral Sciences, 58, 754–764.View ArticleGoogle Scholar
- Hossain, M, & Kauranen, I. (2015). Crowdsourcing: a systematic literature review. Strategic Outsourcing: An International Journal, 8(1), 2–22.View ArticleGoogle Scholar
- Howe, J. (2006). The rise of crowdsourcing. Wired Magazine, 14(6), 1–4.Google Scholar
- Hudson-Smith, A, Batty, M, Crooks, A, & Milton, R. (2009). Mapping for the masses accessing Web 2.0 through crowdsourcing. Social Science Computer Review, 27(4), 524–538.View ArticleGoogle Scholar
- Hutter, K, Hautz, J, Füller, J, Mueller, J, & Matzler, K. (2011). Communitition: the Tension between competition and collaboration in community‐based design contests. Creativity and Innovation Management, 20(1), 3–21.View ArticleGoogle Scholar
- Jouret, G. (2009). Inside cisco’s search for the next big idea. Harvard Business Review, 87(9), 43–45.Google Scholar
- Kaikati, AM, & Kaikati, JG. (2013). Doing Business without exchanging money: the scale and creativity of modern barter. California Management Review, 55(2), 46–71.View ArticleGoogle Scholar
- Karvetski, CW, Olson, KC, Mandel, DR, & Twardy, CR. (2013). Probabilistic coherence weighting for optimizing expert forecasts. Decision Analysis, 10(4), 305–326.View ArticleGoogle Scholar
- Lampe, C, LaRose, R, Steinfield, C, & DeMaagd, K. (2011). Inherent barriers to the use of social media for public policy informatics. The Innovation Journal: The Public Sector Innovation Journal, 16(1), 1–17.Google Scholar
- Lampel, J, Jha, PP, & Bhalla, A. (2012). Test-driving the future: how design competitions are changing innovation. Academy of Management Perspectives, 26(2), 71–85.View ArticleGoogle Scholar
- Lauto, G, Valentin, F, Hatzack, F, & Carlsen, M. (2013). Managers at work: managing front-end innovation through idea markets at Novozymes. Research-Technology Management, 56(4), 17–26.View ArticleGoogle Scholar
- Leimeister, JM, Huber, M, Bretschneider, U, & Krcmar, H. (2009). Leveraging crowdsourcing: activation-supporting components for IT-based ideas competition. Journal of Management Information Systems, 26(1), 197–224.View ArticleGoogle Scholar
- Levine, SS, & Prietula, MJ. (2014). Open collaboration for innovation: principles and performance. Organization Science, 25(5), 1414–1433.View ArticleGoogle Scholar
- Lutz, R. (2011). Marketing scholarship 2.0. Journal of Marketing, 75, 225–234.View ArticleGoogle Scholar
- Marjanovic, S, Fry, C, & Chataway, J. (2012). Crowdsourcing based business models: in search of evidence for innovation 2.0. Science and Public Policy, 39(3), 318–332.View ArticleGoogle Scholar
- Mollick, E. (2014). The dynamics of crowdfunding: an exploratory study. Journal of Business Venturing, 29(1), 1–16.View ArticleGoogle Scholar
- Mollick, ER, & Nanda, R (forthcoming). Wisdom or madness? Comparing crowds with expert evaluation in funding the arts. Management Science. http://pubsonline.informs.org/doi/pdf/10.1287/mnsc.2015.2207.
- Mortara, L, Ford, SJ, & Jaeger, M. (2013). Idea competitions under scrutiny: acquisition, intelligence or public relations mechanism? Technological Forecasting and Social Change, 80(8), 1563–1578.View ArticleGoogle Scholar
- Muhdi, L, & Boutellier, R. (2011). Motivational factors affecting participation and contribution of members in two different Swiss innovation communities. International Journal of Innovation Management, 15(3), 543–562.View ArticleGoogle Scholar
- Nishikawa, H, Schreier, M, & Ogawa, S. (2013). User-generated versus designer-generated products: a performance assessment at Muji. International Journal of Research in Marketing, 30(2), 160–167.View ArticleGoogle Scholar
- Olson, DL, & Rosacker, K. (2013). Crowdsourcing and open source software participation. Service Business, 7(4), 499–511.View ArticleGoogle Scholar
- Poetz, MK, & Schreier, M. (2012). The value of crowdsourcing: can users really compete with professionals in generating new product ideas? Journal of Product Innovation Management, 29(2), 245–256.View ArticleGoogle Scholar
- Rosen, PA. (2011). Crowdsourcing lessons for organizations. Journal of Decision Systems, 20(3), 309–324.View ArticleGoogle Scholar
- Rosengren, KE. (1981). Advances in Scandinavia content analysis: an introduction. In KE Rosengren (Ed.), Advances in content analysis (pp. 9–19). Beverly Hills, CA: Sage.Google Scholar
- Saxton, GD, Oh, O, & Kishore, R. (2013). Rules of crowdsourcing: models, issues, and systems of control. Information Systems Management, 30(1), 2–20.View ArticleGoogle Scholar
- Schumaker, RP. (2013). Machine learning the harness track: crowdsourcing and varying race history. Decision Support Systems, 54(3), 1370–1379.View ArticleGoogle Scholar
- Schweitzer, FM, Buchinger, W, Gassmann, O, & Obrist, M. (2012). Crowdsourcing: leveraging innovation through online idea competitions. Research-Technology Management, 55(3), 32–38.View ArticleGoogle Scholar
- Schwienbacher, A, & Larralde, B. (2012). Crowdfunding of small entrepreneurial ventures. In D Cumming (Ed.), Handbook of entrepreneurial finance, New York. NY: Oxford University Press.Google Scholar
- Shao, B, Shi, L, Xu, B, & Liu, L. (2012). Factors affecting participation of solvers in crowdsourcing: an empirical study from China. Electronic Markets, 22(2), 73–82.View ArticleGoogle Scholar
- Short, JC, Ketchen, DJ, Jr, Shook, CL, & Ireland, RD. (2010). The concept of “opportunity” in entrepreneurship research: past accomplishments and future challenges. Journal of Management, 36(1), 40–65.View ArticleGoogle Scholar
- Simula, H, & Ahola, T. (2014). A network perspective on idea and innovation crowdsourcing in industrial firms. Industrial Marketing Management, 43(3), 400–408.View ArticleGoogle Scholar
- Stieger, D, Matzler, K, Chatterjee, S, & Ladstaetter-Fussenegger, F. (2012). Democratizing strategy: how crowdsourcing can be used for strategy dialogues. California Management Review, 54(4), 44–68.View ArticleGoogle Scholar
- Sun, Y, Fang, Y, & Lim, KH. (2012). Understanding sustained participation in transactional virtual communities. Decision Support Systems, 53(1), 12–22.View ArticleGoogle Scholar
- Wagner, EB. (2011). Why prize? The surprising resurgence of prizes to stimulate innovation. Research-Technology Management, 54(6), 32–36.View ArticleGoogle Scholar
- Weeks, MR, & Veltri, NF. (2013). Virtual communities as narrative networks: developing a model of knowledge creation for crowdsourced environments. International Journal of Knowledge Management, 9(1), 21–41.View ArticleGoogle Scholar
- Wikhamn, BR, & Wikhamn, W. (2013). Structuring of the open innovation field. Journal of Technology Management and Innovation, 8(3), 173–185.Google Scholar
- Yadav, MS. (2010). The decline of conceptual articles and implications for knowledge development. Journal of Marketing, 74(1), 1–19.View ArticleGoogle Scholar
- Zhao, Y, Zhu, Q (2014). Evaluation on crowdsourcing research: Current status and future direction. Information Systems Frontiers, 16(3), 417-434.Google Scholar
- Zheng, H, Li, D, & Hou, W. (2011). Task design, motivation, and participation in crowdsourcing contests. International Journal of Electronic Commerce, 15(4), 57–88.View ArticleGoogle Scholar