Crowdsourcing in business and management disciplines: an integrative literature review

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.


Review method
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 categoriesbusiness and managementto 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, empiricalquantitative 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).   Articles in other categories such as conceptual and managerial have also been published at a reasonable extent. We have found 10 conceptual, 10 qualitative, 18 quantitative, 4 mixed method and 8 managerial articles.  Table 2 around here

Reviews
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.

Conceptual studies
We found 10 conceptual articles in the literature (Table 3). These articles played a pivotal role in theory development on crowdsourcing. Afuah and Tucci (2012) argued that under certain circumstances crowdsourcing transforms distant search into local search. Hence, crowdsourcing is better than internal and contractual search for solving some problems. Avenali et al. (2013) believed that voluntary and legally enforceable agreements, proper evaluation of intellectual property rights, and detailing of economic and technical issues are necessary to induce collaboration between seekers and solvers. Crowdsourcing is useful for problem solving but it is not suitable for capturing value (Bloodgood, 2013). However, Afuah and Tucci (2013) dismiss the fundamental issue raised by Bloodgood (2013) and demonstrate how crowdsourcing can be used to capture value. Crowdsourcing seems to be suitable for both value creation and value capture. Table 3  Rationale for Marketing Scholarship 2.0a more digital, collaborative approach for marketing knowledge production is getting significant (Lutz, 2011).  Table 4 provides a list of 10 qualitative articles. Five of these articles have used single crowdsourcing platform as the data source. Open business model leads to develop a multi-level incentive model (Chanal and Caron-Fasan, 2010). A study by Battistella and Nonino (2012) argue that as the phase moves from foresight to design stage, the more extrinsic motivation is used whereas intrinsic motivation alone is rarely used.

Qualitative studies
Monetary incentive is the most motivational factor on crowdsourcing platforms.
Crowdsourcing can be used even in case of small number of crowds (Bojin et al., 2011). 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.
Please insert Table 4 around here 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. Table 5 includes a list of 18 quantitative articles. In most of these articles, online survey over single crowdsourcing platform is used for data collection. Studying Dell's IdeaStorm community, Bayus (2013) found that serial ideators submit more valuable ideas than single ideators but serial ideators are unlikely to repeat their earlier success once their ideas are implemented. Moreover, crowds' twitter messages can help in disaster management situation (Castillo et al., 2013). Using a case study on human classification scenario, Costa et al. (2013) proposed an active learning framework which allows non-expert classification performed by crowds.

Quantitative studies
Extracting data of Haiti earthquake, Crooks and Wise (2013) (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 ). 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
Only four articles used mixed method (Table 6) 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.

Managerial studies
Eight articles were published with managerial focus (Table 7). Even though some of them contain both qualitative and quantitative data and analysis, the focus of the studies is towards managers; hence we considered them under managerial group. Blohm et al. (2013) showed how firms can build the absorptive capacity -firms' ability to sense, value, assimilate, and apply new knowledgeto capture business value and to find possible ways to overcome challenges for implementing crowdsourcing. Bonabeau (2009) highlighted factors that are crucial to understand if collective intelligence leads to better decisions. To reap benefits from collective intelligence, they suggested that firms need to understand some essential issues such as balance between diversity and expertize, and decentralized and distributed decision making. 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. Table 7 around here 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.

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They claimed that examples such as Wikipedia, Threadlessan online t-shirt selling firm, and Txteaglecell 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 risktakers 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. 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.  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. (2015) 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 Cumming 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 nonfinancial 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.

Discussions
This is a review study on crowdsourcing in business and management disciplines. We found that crowdsourcing is not a concept under the umbrella of open innovation concept (Table 1) Since the crowdsourcing concept has recently emerged in the B&M disciplines, various issues related with crowdsourcing are yet to emerge and many of the emerged issues are yet to pass the passage of rigorous empirical tests.
Please insert Figure 4 around here 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.