When does crowdsourcing benefit firm stock market performance?
Introduction
At the beginning of the 16th century, trade between Europe, the new American, and Indian colonies was flourishing. Transoceanic voyages, however, were exceptionally dangerous at the time, since there was no reliable methodology to determine the exact location of a vessel in the open sea. This situation was referred to as ‘the longitude problem’, which was not resolved until the second half of the 18th century. Remarkably, the solution did not come from a physicist, naval engineer, or leading scientist, such as Isaac Newton, studying this problem at the time. The ‘sea watch’ was instead invented by John Harrison, a working-class clockmaker, who was seduced by the rich monetary reward that the Longitude Act offered anyone who could solve this problem. This law, passed by the British Parliament in July 1714, was the first historical account of what is now referred to as crowdsourcing (Howe, 2006).
Crowdsourcing, which is a particular form of open innovation (OI) (Bogers et al., 2017; Enkel et al., 2009; West et al., 2014) involving dispersed individuals from outside the firm’s boundaries is well described by Brabham (2008) as “the process of posting a problem online, having a vast number of individuals offering solutions and awarding the winning ideas with some form of a bounty”. Recent advancements in information technologies (Ford et al., 2015; Franzoni and Sauermann, 2014; Garcia Martinez, 2015; Majchrzak and Malhotra, 2013; Mina et al., 2014) allow an even larger number of people to be involved in crowdsourcing campaigns, granting access to a wide array of external expertise and knowledge (Bayus, 2013; Schenk and Guittard, 2011). In fact, online participation through web-based platforms facilitates collaborations with widely dispersed individuals by overcoming social, cultural, and geographical barriers (Cappa et al., 2016). While it took the British Parliament over fifty years to solve the longitude problem, crowdsourcing campaigns today can last just a few months, collecting thousands of contributions, i.e., ideas (Bayus, 2013; Brabham, 2008; Howe, 2006; Poetz and Schreier, 2012; Schemmann et al., 2016).
Crowdsourcing may foster the firm’s innovation capacity, since external resources grant access to knowledge, skills, and expertise that are not present within the firm’s boundaries (Afuah and Tucci, 2012; Magnusson, 2009; Poetz and Schreier, 2012; Randhawa et al., 2016; Schemmann et al., 2016; Xu et al., 2015). By extending or renewing the firm’s existing knowledge stocks, the use of external resources from the crowd to find a solution to a given problem can help firms innovate.
However, crowdsourcing differs from other forms of OI for a number of reasons. First, the firm engaging in crowdsourcing typically interacts with a much higher number of outside entities – the crowd oftentimes consists of thousands of dispersed individuals – than in other forms of OI (Afuah and Tucci, 2012; Schenk and Guittard, 2011). For this reason, crowdsourcing also entails distinctive costs related to the resources used to administer the online campaign and to evaluate the submitted proposals (Afuah and Tucci, 2012; Blohm et al., 2013; Caputo et al., 2016). Second, crowdsourcing does not expose firms to disputes related to intellectual property rights on innovations developed based on the contributions collected (Mortara et al., 2013). In fact, participants contributing to a firm’s call for ideas relinquish any rights on the innovation outcomes developed. Finally, crowdsourcing also differs from internal R&D, since its results are based on ideas from an unknown crowd outside the firm’s boundaries rather than on internal efforts, and are hence more uncertain.
As such, the findings from OI and internal R&D studies cannot be directly applied to crowdsourcing. For this reason, research on crowdsourcing has burgeoned in recent years, spanning from studies developing taxonomies of this phenomenon (Blohm et al., 2013; Estellés-Arolas and González-Ladrón-de-Guevara, 2012; Penin and Burger-Helmchen, 2011; Schenk and Guittard, 2011) to research attempting to assess the quality of the contributions collected from crowds (Bayus, 2013; Poetz and Schreier, 2012). Indeed, to our best knowledge, only Xu et al. (2015) attempt to assess the effects of crowdsourcing on firm performance measured in relation to competitors through self-reported assessments. However, their study is limited to the Chinese context and uses a survey-based measure of perceived firm performance. Moreover, they do not analyze the contingencies of the effect of crowdsourcing on firm performance. Thus, we still lack a full understanding of whether and under which conditions crowdsourcing may be beneficial or not to firm performance (Bogers et al., 2010).
To fill this gap, our study aims to answer the following research question: When does crowdsourcing benefit firm stock market performance? To answer this question, similarly to studies that analyze the impact of R&D on firm performance (Kelm et al., 1995; Mc Namara and Baden-Fuller, 2007; Woolridge and Snow, 1990), we focus on the impact of crowdsourcing on the firm’s future profits through a forward-looking measure of firm stock market performance (i.e., stock price reactions to crowdsourcing announcements). To assess the impact of crowdsourcing on a firm’s future profits, we conducted an event study, a methodology widely used to assess the effects of the announcement of a firm’s decisions on its future profits (Faccio and Stolin, 2006; Mc Namara and Baden-Fuller, 2007; Narayanan et al., 2000). The signaling of the announcement of a crowdsourcing campaign positively affects stock prices if conducive to profits. We contend that this occurs when firms are able to extract value from the crowd of dispersed individuals through collecting high quality ideas and effectively processing such ideas for commercial purposes (Shukla et al., 2015).
Thus, the crowd is conceived as a distinctive external resource the firm can use to achieve higher future profits, and we draw on the resource-based view (Barney, 1991; Wernerfelt, 1984) to examine when crowdsourcing allows a firm to extract value from the crowd. Based on this theoretical premise, we argue that it is possible to extract value from the crowd by maximizing the number of individuals responding to the call for ideas, as this will likely lead to a higher number of contributions, in turn leading to the greater quality of the best ones collected (e.g., Boudreau et al., 2011; Sanjiv, 2017; Terwiesch and Xu, 2008), and/or by processing such high quality ideas to innovate and ultimately apply them for commercial ends. More specifically, we argue that two firm-specific factors are crucial for extracting value from the crowd: brand value – which drives the number of potential contributions, thus increasing the likelihood of obtaining ‘extreme outcomes’ (Boudreau et al., 2011), i.e., high quality ideas with exceptional business potential, and investment opportunities – which increase the likelihood that a firm effectively processes such extreme outcomes from the crowd and turns them into profitable innovations. Consistent with our arguments, we find that the effect of crowdsourcing announcements on firm stock market performance is positively affected by higher brand value and higher investment opportunities.
This study makes a number of important contributions to the literature. First, we contribute to crowdsourcing research (Afuah and Tucci, 2012; Bayus, 2013; Blohm et al., 2013; Ford et al., 2015; Garcia Martinez, 2015; Penin and Burger-Helmchen, 2011; Poetz and Schreier, 2012; Schemmann et al., 2016) by analyzing the impact of crowdsourcing on firms’ future profits and challenging the common view that engaging in crowdsourcing is always beneficial for a firm (Rass et al., 2013; Xu et al., 2015; Zhao and Zhu, 2012). Moreover, by providing empirical evidence of the boundary conditions under which crowdsourcing positively affects firm stock market performance, we not only advance the crowdsourcing literature, but also provide practitioners with indications on whether and when to launch crowdsourcing campaigns that have a positive impact on their firm’s performance. Put differently, our findings show that not all firms may profit from crowdsourcing, pointing to the importance of taking into account the firm-specific factors that turn the external crowd into a valuable resource for the firm.
Second, as crowdsourcing is commonly seen as a particular form of OI, our findings respond to recent calls to outline the boundary conditions that make OI beneficial for firms’ performance (Bogers et al., 2017; Chesbrough, 2012). By showing that two firm-specific contingency factors influence the effect of crowdsourcing on firm performance, this study sheds light on the importance of a contingency approach to identify the boundary conditions of the performance implications of different forms of OI, helping to reconcile the mixed findings from prior OI research (Bogers et al., 2017; Caputo et al., 2016).
Finally, while stock market reactions have been used in the context of internal R&D efforts (Mc Namara and Baden-Fuller, 2007; Narayanan et al., 2000), our market-based measure has not yet be applied in the OI field. With this event study, we advance current understanding of how forward-looking market-based measures, such as stock market reactions, can be used to analyze the long-term performance effects of OI activities, complementing alternative measures, such as the short-term performance and survey-based indicators commonly adopted in the OI literature (Ahn et al., 2015; Caputo et al., 2016; Cirillo and Valentini, 2014; Noh, 2015; Xu et al., 2015), thereby paving the way for a more comprehensive examination of the actual impact of OI on a firm’s future profits.
Section snippets
Background and hypotheses
OI can be classified into three categories, depending on the direction of the information flows (Dahlander and Gann, 2010; Enkel et al., 2009; Michelino et al., 2014; West and Bogers, 2014): (i) outside-in, where the firms’ innovativeness benefits from ideas from outside the firm’s boundaries; (ii) inside-out, which implies transferring internal ideas outside the firm’s boundaries; and (iii) coupled, where both outside-in and inside-out flows exist. Crowdsourcing is an outside-in form of OI
Methods and data
To assess the impact of crowdsourcing on a firm’s future profits, we conducted an event study (Faccio and Stolin, 2006; Mc Namara and Baden-Fuller, 2007). This methodology rests on the assumption that capital markets are efficient (Fama, 1970), implying that publicly available information on firms is reflected in their stock market prices. Under this assumption, the firm’s stock market capitalization may be considered a reasonable proxy of its underlying value, which changes only if new
Results
The normality test we conducted confirmed the Gaussian distribution of the sample required for the event study methodology. We report the descriptive statistics and correlations among variables in Table 2, evidencing that multicollinearity is not a problem in the sample. Table 3 reports the results of the OLS models: Model 1 includes only the control variables, Models 2 and 3 show one independent variable at a time (respectively brand value and dividend yield), and Model 4 comprises both the
Discussion
Crowdsourcing is a recent form of outside-in OI used to access the fragmented knowledge of dispersed individuals (Afuah and Tucci, 2012; Garcia Martinez and Walton, 2014; Surowiecki, 2005). Although the crowdsourcing phenomenon has been the focus of a growing body of research, the literature is ambiguous as to whether and when crowdsourcing benefits firms’ performance. Understanding the performance implications of crowdsourcing is therefore an important question that remains unaddressed (Bogers
Conclusion
Crowdsourcing is increasingly adopted in corporate practice to boost innovation. Although the crowdsourcing literature has burgeoned in recent years, the effect of crowdsourcing on a firm’s future profits is far from clear. Through an event study of stock market reactions to crowdsourcing announcements, we offer new insights on the conditions under which crowdsourcing can benefit a firm’s stock market performance. Our findings indicate two key contingency factors, i.e., brand value and
Funding
This work was supported by the “Ministero dell’Istruzione, dell’Università e della Ricerca Italy” under Grant # PRIN 2010H37KAW. Moreover, Francesco Cappa would like to gratefully acknowledge Ermenegildo Zegna for the support received through the Ermenegildo Zegna Founder’s Scholarship 2017–2018 and 2018-2019. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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