DOI: 10.1177/00222429211031814 Author Accepted Manuscript

Do Backer Affiliations Help or Hurt Crowdfunding Success?

Journal: Journal of Marketing PeerManuscript ID ReviewJM.20.0216.R4 Version Manuscript Type: Revised Submission

Research Topics: Co-creation and Crowdsourcing, Innovation and Creativity

Methods: Panel Data Models, Network Models

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1 1 2 Author Accepted Manuscript 3 Do Backer Affiliations Help or Hurt Crowdfunding Success? 4 5 6 7 8 9 10 Abstract 11 12 Crowdfunding has emerged as a mechanism to raise funds for entrepreneurial ideas. On 13 crowdfunding platforms, backers (i.e., individuals who fund ideas) jointly fund the same idea, 14 leading to affiliations, or overlaps, within the community. We find that while an increase in 15 16 the total number of backers may positively affect funding behavior, the resulting affiliations 17 affect funding negatively. We reason that when affiliated others fund a new idea, individuals 18 may feel less of a needPeer to fund, Reviewa process known as Version vicarious moral licensing. Based on data 19 collected from 2,021 ideas on a prominent crowdfunding platform, we show that prior 20 affiliation among backers negatively affects an idea’s funding amount and eventual funding 21 success. Creator engagement (i.e., idea description and updates) and backer engagement (i.e., 22 23 Facebook shares) moderate this negative effect. The effect of affiliation is robust across 24 several instrumental variables, model specifications, measures of affiliation, and multiple 25 crowdfunding outcomes. Results from three experiments, a survey, and interviews with 26 backers support the negative effect of affiliation and show that it can be explained by 27 vicarious moral licensing. We develop actionable insights for creators to mitigate the 28 negative effects of affiliation with the language used in idea descriptions and updates. 29 30 31 32 Keywords: crowdfunding, backer affiliation, social structure, prosocial, vicarious moral 33 licensing 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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2 1 2 Author Accepted Manuscript 3 Crowdfunding has emerged as a dominant mechanism to harness the power of crowds in 4 5 6 raising funds for innovative ideas. Interest in crowdfunding has surged in recent years. Facebook 7 8 acquired Oculus 3D visualization device, a crowdfunded idea on Kickstarter, for 3 billion USD 9 10 (Durbin 2017). Peloton, the highly successful exercise bike, started as a Kickstarter project. The 11 12 global crowdfunding market is expected to be well over 40 billion USD by 2026 (Statista 2021). 13 14 15 Brands such as GE (Cowley 2016) and Unilever use crowdfunding to spur innovation (Stalder 16 17 and Stenson 2016), andPeer academic Review research on the phenomenon Version and its role in the digital 18 19 economy is emerging (Allen et al. 2018; Dai and Zhang 2019). Crowdfunding is a form of 20 21 22 crowdsourcing in which participants, henceforth referred to as backers, are recruited to raise 23 24 funds for ideas (e.g., Fan et al. 2020; Wei et al. 2021). As some backers fund the same ideas, i.e., 25 26 co-back, overlaps develop between these backers. These overlaps, called affiliations, are key 27 28 29 building blocks of the community’s network structure and have been studied in other 30 31 crowdsourcing communities (e.g., Ransbotham, Kane, and Lurie 2012). In this research, we 32 33 explore how affiliation, defined as the number of co-backing relationships between potential 34 35 backers and those who have previously funded the focal idea, might affect the idea’s 36 37 38 crowdfunding success. We illustrate affiliation in crowdfunding using a stylized example in 39 40 Figure 1. 41 42 [Insert Figure 1 about here] 43 44 45 We know that crowd size affects outcomes positively as participants look to anonymous 46 47 others for cues to decide which ideas to fund, a phenomenon referred to as herding (e.g., Zhang 48 49 and Liu 2012). Past research shows that attracting more backers positively impacts crowdfunding 50 51 52 outcomes (Hou et al. 2020), an insight that many creators seem to grasp. However, crowd size 53 54 does not represent the social structure, i.e., the pattern of connections in the community. In 55 56 57 58 59 60 Journal of Marketing Page 3 of 80

3 1 2 Author Accepted Manuscript 3 crowdfunding, as in other contexts where shared communal goals exist (e.g., Wikipedia), social 4 5 6 structure plays a more prominent role (e.g., Ransbotham et al. 2012; Wei et al. 2021). 7 8 Our primary contribution is in showing that while the total number of backers, i.e., 9 10 crowd size, may positively affect funding behavior and idea success (e.g., Zhang and Liu 2012), 11 12 adding backers may not be unilaterally beneficial as the ensuing affiliation between backers 13 14 15 negatively affects funding. Our analysis reveals that the negative effect of backer affiliation is 16 17 above and beyond thePeer positive effect Review of number of backers, Version i.e., the herding effect, highlighting 18 19 the tension between the benefits of adding more backers and the adverse effects of backer 20 21 22 affiliation. In other words, while adding a new backer (e.g., the focal backer in Figure 1) may 23 24 positively affect the focal idea’s success, adding this focal backer may not be equally beneficial 25 26 across the three scenarios in Figure 1 as the degree of affiliation differs. We propose that the 27 28 29 affiliation between the focal backer and other backers will influence the amount that the focal 30 31 backer puts toward the focal idea and, hence, the idea’s funding success. 32 33 Affiliation is a powerful force as it makes affiliated others’ actions lead to changes in 34 35 one’s subsequent behavior (e.g., Mallapragada et al. 2012; Sunder et al. 2019). In some contexts, 36 37 38 affiliation positively affects behavior as individuals desire to belong and therefore conform to 39 40 affiliated others’ behavior (e.g., Leary 2010). However, in crowdfunding communities where 41 42 individuals are often motivated by prosocial goals (e.g., Simpson et al. 2021), we propose that 43 44 45 such affiliation can negatively affect behavior. When individual actions benefit a social cause, 46 47 seeing affiliated others participate may make individuals feel less of a need to do so, a process 48 49 referred to as vicarious moral licensing (e.g., Decety and Grèzes 2006; Goldstein and Cialdini 50 51 52 2007; Meijers et al. 2019). Thus, we propose that when backers decide whether to fund an idea, 53 54 55 56 57 58 59 60 Journal of Marketing Page 4 of 80

4 1 2 Author Accepted Manuscript 3 they are less likely to do so, or more likely to fund a lower amount, if affiliated others have 4 5 6 already done so. 7 8 While affiliations develop in the community through co-backing, creators and backers 9 10 also engage through non-monetary actions, thereby driving social interaction. Therefore, to 11 12 develop further substantive implications about the effect of affiliation, we examine the 13 14 15 moderating role of both creator and backer engagement (e.g., Bayus 2013; Mallapragada et al. 16 17 2012). For example,Peer creators communicate Review with backers Version through the description of the idea on its 18 19 homepage, perceived to be an important determinant of an idea’s success (Moradi and Dass 20 21 22 2019; Xiang et al. 2019), and by posting updates about progress. Backers engage with the 23 24 community by sharing ideas on social media. We seek to understand how the effect of affiliation 25 26 varies due to creator and backer engagement, as they help shed light on the underlying 27 28 29 mechanism that drives the effect of affiliation. 30 31 We use multiple methods and datasets, including secondary data and experiments, to 32 33 provide convergent validity to our findings. We also conduct interviews with six backers, survey 34 35 100 backers, and analyze 572 posts on backer forums to develop insights about the mechanism 36 37 38 driving the effect of affiliation on funding outcomes. First, we assemble a comprehensive dataset 39 40 of daily funding for 2,021 new crowdfunded ideas listed on Kickstarter. We study two 41 42 crowdfunding outcomes: 1) the monetary amount of funding received by an idea on any given 43 44 45 day and 2) whether the idea raises sufficient funds during the funding window to meet or exceed 46 47 its funding goal. We measure affiliation of an idea on the focal day as the number of co-backing 48 49 relationships of backers who back on the focal day, with backers who funded until the day before 50 51 52 the focal day (e.g., Mallapragada et al. 2012; Narayan and Kadiyali 2016). We estimate an 53 54 instrumental variables regression model with fixed effects to assess the impact of affiliation 55 56 57 58 59 60 Journal of Marketing Page 5 of 80

5 1 2 Author Accepted Manuscript 3 among an idea’s backers on the daily funding amount and report results from several robustness 4 5 6 analyses. Second, we present results from controlled experiments, where we exogenously 7 8 manipulate affiliation, and across three experiments, we replicate the negative effect of affiliation 9 10 on funding, examine the underlying mechanism, and uncover the role of a key moderator. We 11 12 find that the negative effect is stronger when creators use more communal words—both in the 13 14 15 initial description of the idea and in subsequent updates—and when more backers share the idea 16 17 on social media. Thus,Peer creator and Review backer engagement Version may moderate prosocial motives to fund, 18 19 further validating the proposed licensing mechanism. 20 21 22 We make several contributions. For the first time, we show that affiliation among backers 23 24 affects crowdfunding success in statistically and economically significant ways after controlling 25 26 for herding and accounting for several alternative explanations. A 10% daily increase in number 27 28 29 of backers would lead to an additional 20.2% in funding or an increase of 83 USD/day (i.e., the 30 31 herding effect). In contrast, a 10% daily increase in backer affiliation would lead to an 8.7% 32 33 decrease in funding or a decrease of 36 USD/day, offsetting the increase due to number of 34 35 backers by about 43%. Thus, adding backers is good, but if the additional backers increase 36 37 38 affiliation, the positive effect of adding these backers is smaller in the scenario when affiliation is 39 40 high. We isolate vicarious moral licensing as a theoretical mechanism that drives the negative 41 42 effect of affiliation through experiments. We explore the role of factors related to the idea, the 43 44 45 creator, and the backers, all of which interact with affiliation. 46 47 Theoretical Background 48 49 Social Influences in Crowdfunding 50 51 52 While crowdfunding emerges as a dominant force for funding new ideas, research on 53 54 crowdfunding is limited. Most early research focused on microlending (Lin et al. 2013; Zhang 55 56 57 58 59 60 Journal of Marketing Page 6 of 80

6 1 2 Author Accepted Manuscript 3 and Liu 2012) or on crowdfunding platforms for music and journalism (e.g., Agrawal et al. 2015; 4 5 6 Burtch et al. 2013). Topics such as proximity to the deadline (Dai and Zhang 2019) and the text 7 8 of content (e.g., Netzer et al. 2019) have also gathered attention. Researchers have studied a 9 10 variety of social factors that influence crowdfunding, in particular, the relationship between 11 12 creators and individual backers, including the role of offline friendship (Lin et al. 2013), 13 14 15 geographic proximity (Agrawal et al. 2015), and social interactions (Kim et al. 2020). We 16 17 present a summary ofPeer representative Review research in Table Version 1. 18 19 -- Insert Table 1 about here -- 20 21 22 In addition to the relationship between creators and backers, there are several ways in 23 24 which others’ actions might inform backers’ funding decisions. For example, Zhang and Liu 25 26 (2012) report that potential lenders assess borrowers’ creditworthiness by observing other 27 28 29 lenders. They attribute the positive effect of the number of other lenders to herding, wherein 30 31 crowd size becomes a beacon for others to decide which ideas to fund. This finding might 32 33 suggest that the mere addition of more supporters unilaterally benefits crowdfunding outcomes 34 35 as potential backers simply follow other backers. What are some factors that might limit the 36 37 38 positive impact of the crowd’s behavior on crowdfunding? To answer this question, we note that 39 40 most research has considered the presence of the anonymous crowd as the cause for a social 41 42 effect that is generally positive. However, crowd size does not account for an important aspect of 43 44 45 networks, i.e., the structure of connections among the community’s participants. 46 47 Thus, what is missing in extant research is an explicit acknowledgment of social structure 48 49 beyond crowd size and an exploration of how it impacts crowdfunding outcomes. Social 50 51 52 structure arises due to co-participation in events, in our case, co-backing across ideas, a 53 54 phenomenon referred to as affiliation (e.g., Faust 1997; Wasserman and Faust 1999). Affiliation, 55 56 57 58 59 60 Journal of Marketing Page 7 of 80

7 1 2 Author Accepted Manuscript 3 identified as an important phenomenon in the new digital economy dominated by crowdsharing 4 5 6 (Eckhardt et al. 2019), is the central focus of our research. 7 8 Affiliation in Crowdfunding 9 10 Communities evolve through repeated interactions between members, which give rise to 11 12 affiliations or overlaps. As affiliations grow, the interconnectivity among backers leads to 13 14 15 scaffolding structures that hold the community together through both first- and second-order ties. 16 17 Affiliations have beenPeer studied in Reviewinterfirm relationships Version (Swaminathan and Moorman 2009), 18 19 board interlocks (Srinivasan et al. 2018), product development (e.g., Mallapragada et al. 2012), 20 21 22 and wiki contributions (Ransbotham et al. 2012). Regardless of the context, research suggests 23 24 that 1) individuals notice affiliated others’ behavior, 2) individuals feel a sense of connectedness 25 26 and shared identity with affiliated others, and as such 3) affiliated others’ actions lead to changes 27 28 29 in one’s subsequent behavior (e.g., Mallapragada et al. 2012; Sunder et al. 2019). 30 31 To establish that participants notice affiliated others’ behavior when visiting 32 33 crowdfunding platforms, we ran a pilot study with actual backers who we pre-screened based on 34 35 prior crowdfunding behavior. Participants were shown a screenshot of a crowdfunding page 36 37 38 created by a web designer. To assess which information captured participants’ attention, we used 39 40 a standard heat-mapping approach for measuring visual attention (Berger et al. 2012). Invisible 41 42 boxes around various pieces of information (e.g., idea title, backer information, idea description) 43 44 45 coded visual attention as participants read and clicked on information, as per instructions. We 46 47 found that many participants read and clicked on backer information, more so than other 48 49 potentially relevant information such as the number of shares and creator information. Further, of 50 51 52 the available backer information, affiliation ranked as highly important (see Web Appendix A for 53 54 details). Discussions on crowdfunding message boards and websites, and results from a survey 55 56 57 58 59 60 Journal of Marketing Page 8 of 80

8 1 2 Author Accepted Manuscript 3 that we conducted (discussed in the following sections) further support this idea, suggesting that 4 5 6 among all available information, backers do consider affiliated others’ behavior as they make 7 8 funding decisions. Next, to confirm that affiliation affects perceptions of connectedness and 9 10 shared identity in crowdfunding communities, we ran a pilot study with 150 Amazon Turk 11 12 participants. We find that affiliation significantly increased perceptions of connectedness and 13 14 15 shared identity with other backers (see Web Appendix B). 16 17 If potential backersPeer notice Review affiliated others’ behaviors Version and feel a sense of connectedness 18 19 with these affiliated others, how might affiliated others’ behavior influence their own funding 20 21 22 decisions? To answer this important question, we next examine crowdfunding platforms and how 23 24 they differ from non-communal (i.e., transactional) contexts. 25 26 Vicarious Moral Licensing in Crowdfunding Communities 27 28 29 Crowdfunding is a communal endeavor in which individuals collaborate to achieve 30 31 shared goals, and platforms grow due to members’ participation and interactions (Simpson et al. 32 33 2021). Individuals behave differently in communal contexts than in non-communal (i.e., 34 35 transactional) contexts (e.g., Clark and Mils 1993; Fiske 1992). For example, in communal 36 37 38 contexts, individuals are more likely to request help from others, keep track of others’ needs, 39 40 respond to them, and report more positive emotions while doing so (Fiske 1992). In 41 42 crowdfunding, these prosocial goals are reflected in the desire to help others, achieve funding 43 44 45 goals, and be part of a community (Gerber and Hui 2014). 46 47 In crowdfunding communities where individuals are often motivated by prosocial goals 48 49 (e.g., Simpson et al. 2021), we propose that seeing affiliated others fund may make individuals 50 51 52 feel less of a need to do so, a process referred to as vicarious moral licensing (e.g., Decety and 53 54 Grèzes 2006; Goldstein and Cialdini 2007; Meijers et al. 2019). Vicarious moral licensing occurs 55 56 57 58 59 60 Journal of Marketing Page 9 of 80

9 1 2 Author Accepted Manuscript 3 when individuals see affiliated others’ actions as satisfying their own goals, which changes their 4 5 6 perceived moral imperative and subsequent behavior. For example, learning that affiliated others 7 8 demonstrate environmentally friendly behavior makes individuals less likely to do so (Meijers et 9 10 al. 2019). It is important to recognize that this effect is not merely akin to strangers’ behavior in a 11 12 crowd, i.e., the bystander effect (e.g., Kuppuswamy and Bayus 2017), but that it is those with 13 14 15 whom an individual perceives a social connection, i.e., affiliation, that drives the focal effect. 16 17 To confirm thePeer importance Review of affiliated others’ Version behavior and further validate the proposed 18 19 mechanism, we conducted in-depth interviews of six backers, surveyed 100 backers, and coded 20 21 22 572 posts from Kickstarterforum.org, the dominant crowdfunding discussion forum (see Web 23 24 Appendix C). The findings confirmed the prominence of prosocial (i.e., communal) motives on 25 26 crowdfunding decisions, the importance of affiliated others’ behavior on backers’ own funding 27 28 29 behavior, and the role of vicarious moral licensing. For example, as one interview participant 30 31 explained, “I look at other funders only to further discover related projects. It’s an interesting 32 33 way to discover—because some people are more involved than you are … It’s interesting to 34 35 follow that rabbit trail and see ‘oh this person supported this and look at what else they fund’.” 36 37 38 Another stated, “You are dealing with finite resources in terms of what you are willing to spend. 39 40 If you support one thing, I don’t know, for me, if I see someone supporting something else, I 41 42 think, well yeah, they supported that. I’m sure I could find a bunch of other people that support a 43 44 45 bunch of other things. I just gave X amount of dollars, whatever amount I have, and I’m not 46 47 going to be giving any more than that right now.” 48 49 Although we propose vicarious moral licensing as the mechanism underlying the focal 50 51 52 effect of affiliation and initial evidence indicates this to be the case, we acknowledge the 53 54 complexity of social interactions in crowdfunding. Because these social interactions are likely to 55 56 57 58 59 60 Journal of Marketing Page 10 of 80

10 1 2 Author Accepted Manuscript 3 be subject to several factors, we consider uniqueness as an alternative explanation for the 4 5 6 negative effect of affiliation on funding. Backers may seek to identify ideas that have received 7 8 less funding from affiliated others. By doing so, backers can distinguish themselves from these 9 10 affiliated others, fulfilling a need for uniqueness (e.g., Tian et al. 2001). In our analysis, we 11 12 report results from an experiment where we test vicarious moral licensing and uniqueness as 13 14 15 potential explanations for the negative effect of affiliation on funding. 16 17 Moderators of the EffectPeer of Affiliation Review on Crowdfunding Version Success 18 19 Crowdfunding platforms are characterized by contributions from both creators and 20 21 22 backers (e.g., Bayus 2013; Ransbotham et al. 2012) as these interactions create and sustain the 23 24 community’s viability. Therefore, we explore the role of creator and backer engagement in 25 26 moderating the impact of affiliation on crowdfunding. 27 28 29 Previous research has found that while prosocial goals may be common in crowdfunding 30 31 platforms (e.g., Simpson et al. 2021), an idea’s description can further induce prosocial 32 33 motivation and behavior when it emphasizes communal language (Hong et al. 2018). We 34 35 propose that the vicarious moral licensing effect, i.e., the negative effect of affiliation on 36 37 38 funding, is driven by the communal context and the prosocial behavior it prompts and that this 39 40 behavior is further heightened by creators describing their ideas with communal words like 41 42 “together” and asking backers to “partner” with them by providing financial “support” 43 44 45 (Pietraszkiewicz et al. 2017). As such, ideas described with more (vs. less) communal words will 46 47 exhibit a stronger negative effect of affiliation on funding outcomes. 48 49 Creators can also engage with the backer community by posting updates to highlight their 50 51 52 strategic goals and the idea’s progress. Updates provide diagnostic information concerning an 53 54 idea’s success (e.g., Bayus 2013; Mallapragada et al. 2012). Updates might draw backers’ 55 56 57 58 59 60 Journal of Marketing Page 11 of 80

11 1 2 Author Accepted Manuscript 3 attention to the idea’s characteristics and evolution, and lessen attention towards co-backers and 4 5 6 affiliation. Consistent with the vicarious moral licensing mechanism, we expect updates that use 7 8 more (vs. less) communal words to strengthen affiliation's negative effect. As such, we estimate 9 10 the moderating effects of communal words in the creator’s updates. 11 12 We also explore how backers’ engagement might moderate the affiliation effect by 13 14 15 exploring the role of social media sharing of the focal idea by backers. While sharing behavior 16 17 on social media couldPeer have several Review motivations, altruism Version is perceived as a primary motivator, 18 19 and others seeing the shares likely view them as such (Kim and Yang 2017; Li and Wang 2019). 20 21 22 We expect that such sharing heightens funders’ prosocial motives and vicarious moral licensing, 23 24 further strengthening the negative effect of affiliation. 25 26 Next, we describe our data and methodology. 27 28 29 Data and Methodology 30 31 We employed a multi-method approach to investigate the phenomenon. We collected and 32 33 analyzed two types of data—observational data from a crowdfunding platform and experimental 34 35 data from lab settings. We begin by describing the observational data, the empirical model and 36 37 38 identification strategy, the results, and robustness checks. Then, we describe three experiments 39 40 where we identify the primary effect in a controlled setting and shed light on the mechanism 41 42 underlying the primary effect and its moderator. The first experiment demonstrates the negative 43 44 45 effect of affiliation on funding behavior. The second experiment validates vicarious moral 46 47 licensing as an underlying mechanism and rules out uniqueness as one potential alternative 48 49 explanation. The third experiment examines how the idea’s description moderates the effect of 50 51 52 affiliation. 53 54 Collection and Analysis of Observational Data 55 56 57 58 59 60 Journal of Marketing Page 12 of 80

12 1 2 Author Accepted Manuscript 3 We collected data on Kickstarter, the world’s largest and most prominent crowdfunding 4 5 6 platform. We utilized a web crawler to visit the new ideas page listed on Kickstarter beginning 7 8 18th December 2013. From that day and every subsequent day of data collection, the crawler 9 10 visited the pages of the ideas that were started on the first day of the crawl, in addition to all the 11 12 ideas that were started on the subsequent days. We stopped the crawler after 37 days, giving us 13 14 15 data on 2,021 new ideas. We acknowledge that our research’s funding constraints affected the 16 17 number of days, butPeer we went one Review week past the most Version common deadline of 30 days. We note that 18 19 while some ideas in our sample received funding after data collection stopped, our results are 20 21 1 22 robust to this truncation . 23 24 For the data collection, the crawler began with ideas that started receiving funds on the 25 26 day of the crawl, and it identified every backer who funded the focal idea, the funded amount, 27 28 29 and the calendar date. The crawler then visited every backer’s history and collected information 30 31 on all the other ideas that the backer had funded in the past. At the time of data collection, 32 33 Kickstarter made all backers visible to all prospective backers. The list of backers on Kickstarter 34 35 was available by clicking the link “community” that prominently appears on the focal idea’s web 36 37 38 page2. This process allowed us to construct the network, giving us the structure of relationships 39 40 to calculate affiliation. The crawler also collected other relevant information from the page, 41 42 including the idea’s description, number and text of updates, and the number of Facebook shares 43 44 45 of the idea to measure backer engagement. 46 47 Our unit of analysis for the daily amount funded is an idea-day, and our final sample had 48 49 32,438 observations at the idea-day level. This specification makes the most sense because, for a 50 51 52 53 54 1 Results for this analysis are available from the authors upon request. 55 2 On many platforms such information is readily available on the idea’s home page, e.g., https://gogetfunding.com/ 56 and https://www.piggybackr.com/. 57 58 59 60 Journal of Marketing Page 13 of 80

13 1 2 Author Accepted Manuscript 3 dataset with idea-day-backer as the unit of analysis, the funded amount (for an idea on a day) 4 5 6 takes zero values for over 99.9% of observations, making such a specification non-informative. 7 8 Next, we describe the key measures. 9 10 Daily amount funded. Consistent with prior literature (Agrawal et al. 2015; Burtch et al. 11 12 2013), our funding success measure is the amount of funding received by an idea on any given 13 14 15 day. Across all crowdfunding platforms, this measure is always easily and prominently visible on 16 17 the idea’s webpage. PeerSubsequently, Review we show that our Versionresults are robust to other measures of 18 19 success. 20 21 22 Affiliation. Consistent with prior literature (e.g., Mallapragada et al. 2012; Narayan and 23 24 Kadiyali 2016), we posit that two backers are affiliated if they have funded at least one common 25 26 idea on the platform and are not affiliated if all the ideas that they have funded are mutually 27 28 29 exclusive. Thus, the backer affiliation for a focal idea on a focal day is the number of co-backing 30 31 relationships between those backers who fund the focal idea on the focal day and all backers who 32 33 have funded the focal idea at any time before the focal day. 34 35 Consider a backer of a focal idea who funds the focal idea on the focal day. Consider 36 37 38 another backer of the focal idea who funds the focal idea any time before the focal day. A co- 39 40 backing relationship exists between these two backers if they have both funded one idea (other 41 42 than the focal idea) any time before the focal day. One co-backing relationship represents one 43 44 45 unit of affiliation. Affiliation increases both with the number of backers who co-back and with 46 47 the number of co-backed ideas. 48 49 To elaborate, consider the following examples. In each example, idea i is launched on 50 51 52 day t = 1, say December 13. Further, Jack funds idea i on December 17 (t = 5), and the goal is to 53 54 calculate affiliation as of December 17 (t=5). 55 56 57 58 59 60 Journal of Marketing Page 14 of 80

14 1 2 Author Accepted Manuscript 3 Example 1: Tom funds idea i on December 13. Also, before December 17, Jack and Tom both 4 5 6 funded another idea j. As there is one co-backing relationship (that between Jack and Tom for 7 8 co-backing idea j), Affiliationi,t=5 = 1. 9 10 Example 2: Tom funds idea i on December 13. Jack and Jill both fund idea i on December 17. 11 12 Before December 17, Jack and Tom both funded another idea j. Furthermore, before December 13 14 15 17, Jill and Tom both funded another idea k. As there are two co-backing relationships (those 16 17 between Jack and TomPeer for co-backing Review idea j, and between Version Jill and Tom for co-backing idea k), 18 19 Affiliation = 2. 20 i,t=5 21 22 Example 3: Tom funds idea i on December 13. Jane funds idea i on December 14. Before 23 24 December 17, Tom, Jack, and Jane funded another idea j. As there are two co-backing 25 26 relationships (those between Jack and Tom for co-backing idea j, and between Jack and Jane for 27 28 29 co-backing idea j), Affiliationi,t=5 = 2. 30 31 We present summary statistics for Kickstarter in Table 2. The median number of backers 32 33 who fund an idea in a day is 1, and most ideas only have a few backers. When a backer funds an 34 35 idea, the median number of past backers of that idea is 10 backers (i.e., the median of the 36 37 38 variable cumulative number of backers funding idea i before day t is 10). In the 6 months 39 40 preceding data collection, 82% of backers in our data had not funded any idea on Kickstarter. 41 42 Thus, the odds of having to remember multiple co-backing relationships are relatively low. Most 43 44 45 importantly, the median value of affiliation is zero, and the mean is 3.3. In other words, a large 46 47 majority of backers in our data must process a very small amount of information to infer 48 49 affiliation. Our measure of affiliation reflects a more nuanced and disaggregated 50 51 52 conceptualization of affiliations than the number of “co-backed ideas” or the number of 53 54 55 56 57 58 59 60 Journal of Marketing Page 15 of 80

15 1 2 Author Accepted Manuscript 3 “common backers.” Other measures are likely sparser than our measure. Later, we show that our 4 5 6 results are robust to alternate measures of affiliation. 7 8 Updates. We measure the creator’s engagement using the number of creator’s updates on 9 10 the idea page and separately measure the level of communal content in each update. To code 11 12 communal words, we created a dictionary to capture words that reflect the use of communal 13 14 15 language. For this, we asked two graduate research assistants to read descriptions of a random 16 17 sample of 100 ideas Peer(from our data) Review and identify words Version that reflected a “communal” idea while 18 19 coding each description on whether it was communal. We provided the Merriam-Webster 20 21 22 definition of communal—“of or relating to a community”—to the two coders along with 23 24 synonyms from a thesaurus. Then, we cross-verified these words with LIWC’s category for 25 26 “affiliation,” comprising 248 words (Pennebaker et al. 2015). Communal words that appear at 27 28 29 least once in our corpus are member, team, group, groups, family, friends, affiliation, affiliate, 30 31 relation, connection, alliance, relationship, partner, partners, partnership, link, merge, cooperate, 32 33 cooperation, together, join, thanks, “thank you”, appreciate, our, and we. 34 35 We measure backer engagement as the number of Facebook shares of the idea by 36 37 38 backers, which we collected when the web crawler visited an idea’s webpage. 39 40 Model-free evidence. To explore model-free evidence, we present summary statistics 41 42 about three regimes of the distribution of the amount of daily funding achieved for Kickstarter in 43 44 45 Table 3. These regimes are 1) idea-day specific observations when there is no funding, 2) when 46 47 the daily funding is positive but does not exceed the mean level in the data ($409.34), and 3) 48 49 when the daily funding exceeds the mean level. Backer affiliation is highest when ideas do not 50 51 52 receive any funding and lowest when ideas achieve the highest funding. The measure of backer 53 54 affiliation for an idea on a given day is based on co-backing relationships of backers that fund 55 56 57 58 59 60 Journal of Marketing Page 16 of 80

16 1 2 Author Accepted Manuscript 3 the idea on that specific day with backers who funded before that day. If no backer funds on a 4 5 6 specific day, the affiliation measure for that day is zero. The measure is not cumulative, and it 7 8 does not increase over time. Thus, there is model-free evidence for the negative effect of 9 10 affiliation on funding outcomes. We collected similar data from another crowdfunding platform, 11 12 Indiegogo, which we use in the robustness analysis. Additional details about the Kickstarter data 13 14 15 and summary statistics for the Indiegogo data appear in Web Appendix D. We illustrate 16 17 affiliation in FiguresPeer W1-W5, and Review the sample’s network Version structure and growth in Figures W6-W8 18 19 in Web Appendix E. We estimate the primary empirical model on Kickstarter data. 20 21 22 -- Insert Table 2 and Table 3 about here – 23 24 Empirical model. Following Burtch et al. (2013) and Zhang and Liu (2012), our primary 25 26 dependent variable ( y ) is the monetary funding received by an idea i (i = 1,…N) on day t (t = 27 it 28 29 1,…Ti). As a starting point, we incorporate backer affiliation and several controls in a fixed 30 31 effects regression model as follows: 32 33 34 log(yit) = i + t + 1log(Affilit-1) + 2log(CumBackersit-1) + 3log(CumUpdatesit-1) + 4log(yit-1) + 35 5PropGoalit-1 + 6PropDurationit-1 + 7LastWeekit-1 + 8Networkit-1+ 36 9log(CommunalUpdatesit-1)+ 10log(Affilit-1) X CommunalUpdatesit-1 + 11log(Affilit-1) X 37 FBShares +  (1) 38 i it 39 40 To account for non-negativity, we log-transform all variables that are not proportions. For 41 42 variables that can take zero values, we take the logarithm of the variable added to 0.001. 43 44 Replacing this constant with other constants does not affect our results. Estimating the model 45 46 47 without taking logarithms of any variable gave us consistent results. 48 49 To control idea-specific confounding factors such as inherent differences in idea quality, 50 51 the novelty of idea description, creator expertise, etc., we employ idea-specific fixed effects i, a 52 53 54 vector of 2,021 elements for the Kickstarter dataset. We incorporate fixed effects for each day in 55 56 the idea's funding window to control temporal patterns in funding and changes in the Kickstarter 57 58 59 60 Journal of Marketing Page 17 of 80

17 1 2 Author Accepted Manuscript 3 environment over time. These are denoted by the vector  . Error terms are assumed normally 4 t 5 6 distributed and clustered at the idea level. 7 8 Our key independent variable is Affilit-1. This is the number of co-backing relationships 9 10 between those backers who fund idea i on day t-1 and all backers who have funded this idea 11 12 13 before day t-1. Later we report robustness checks to alternate measures of backer affiliation. 14 15 Although our fixed effects specification controls for confounds at the idea level and the day 16 Peer Review Version 17 level, we need to control idea-specific factors that are time-varying. Chief among these is the 18 19 20 amount of funding received by the focal idea on day t-1 (Burtch et al. 2013), enabling us to 21 22 control those time-varying idea-specific unobservables, which may be serially correlated (e.g., 23 24 word of mouth about the idea) and to attenuate serial correlation among the residuals. This also 25 26 accounts for the alternate explanation that affiliation on day t-1 affects funding on day t-1, but 27 28 29 not on day t. By incorporating the lagged measure of funding, we can account for all factors that 30 31 affect funding until the day t-1. 32 33 We now discuss other time-varying idea-specific controls. First, the number of 34 35 36 affiliations among backers is correlated with the number of backers. There can be no backer 37 38 affiliations without backers; more backers could result in more possibilities for affiliation. To 39 40 control for the possibility that the number of backers drives the effect of affiliation on funding, 41 42 43 we include CumBackersit-1, the cumulative number of backers funding idea i by day t-1, as a 44 45 control variable. Also, to the extent that ideas with more backers attract more funding (Zhang 46 47 and Liu 2012), this serves as a measure for herding behavior. Second, creators communicate with 48 49 backers via updates, a means to elevate idea visibility and signal effort (Dai and Zhang 2019). To 50 51 52 understand how creator actions might drive funding, we include CumUpdatesit-1, the cumulative 53 54 55 56 57 58 59 60 Journal of Marketing Page 18 of 80

18 1 2 Author Accepted Manuscript 3 number of updates by the creator of the idea i by day t-1. Additionally, CommunalUpdatesit-1 is 4 5 6 the number of communal words contained in the updates. 7 8 Third, the funding window of an idea influences its funding outcomes. Ideas receive 9 10 higher funding in the later stages of the funding window as the funding deadline nears (e.g., Dai 11 12 and Zhang 2019; Kuppuswamy and Bayus 2017). To account for this, we include the duration of 13 14 15 the funding window completed for the idea (PropDurationit-1) as a proportion of the total funding 16 17 window (typically 30Peer days). Furthermore, Review ideas receive Version greater funding as they get closer to 18 19 meeting their funding goals (Dai and Zhang 2019). Although daily fixed effects account for 20 21 22 temporal variations in funding, they might not capture the effect of proximity to the funding 23 24 goal. Therefore, we include PropGoalit-1, the proportion of the funding goal of the idea i, which 25 26 has been achieved until day t-1, and LastWeekit-1, a dummy variable for whether the observation 27 28 29 belongs to the last week of the funding window. 30 31 Finally, structural measures of network centrality might affect the outcome. As these 32 33 measures capture the extent of social capital that accrues to ideas due to being associated with 34 35 certain backers, we seek to control for the effects of these measures. We compute and include 36 37 38 three of the most widely used network measures in marketing (e.g., Mallapragada et al. 2012; 39 40 Ransbotham et al. 2012; Swaminathan and Moorman 2009), (Networkit-1): Closeness Centrality, 41 42 Betweenness Centrality, and Eigenvector Centrality of idea i on day t. Closeness centrality in our 43 44 45 context is how close the focal idea is from all the backers (connected and not connected) in the 46 47 network, betweenness centrality is the extent to which the focal idea lies on the common paths 48 49 between all pairs of backers in the network, and eigenvector centrality is the extent to which the 50 51 52 focal idea’s backers are prolific in backing other ideas. We computed both bipartite and single- 53 54 55 56 57 58 59 60 Journal of Marketing Page 19 of 80

19 1 2 Author Accepted Manuscript 3 mode network variants of each of these measures3. Given the high correlation across the bipartite 4 5 6 and single-mode versions of each measure, we included in the model that version of each 7 8 measure, which leads to a more significant improvement in R2. As shown in the correlation 9 10 matrix of all variables (Table 4), these variables are not highly correlated with our measure of 11 12 affiliation, suggesting that affiliation captures the network’s unique structural properties based on 13 14 15 counts of overlaps. To assess interaction effects, we interact affiliation with CommunalUpdatesit- 16 4 Peer Review Version 17 1 , and with the number of Facebook shares of the idea by backers (FBSharesi). 18 19 We use lags of all covariates as information about the focal day is not updated in real- 20 21 5 22 time and is unavailable until the following day . We present the correlation matrix of all 23 24 variables in Table 4; most correlations are less than 0.3, allaying multicollinearity’s ill-effects. 25 26 -- Insert Table 4 about here -- 27 28 29 Empirical strategy. We first discuss how our work is different from the peer effects 30 31 literature and then explain our identification strategy. The prototypical problem in the marketing 32 33 literature on the identification of peer effects (e.g., Nair et al. 2010) is to estimate the likelihood 34 35 of agent A adopting a product (e.g., buying an online game) under the knowledge that agent B (a 36 37 38 self-identified “friend” or influencer) has already adopted that same product. The herding 39 40 literature has quite conclusively documented positive peer effects across various consumer 41 42 contexts (e.g., Sunder et al. 2019; Zhang and Liu 2012). If there are positive effects due to the 43 44 45 46 3 47 Clustering coefficient, a related network measure deserves mention. A node's clustering coefficient is the 48 proportion of nodes in its neighborhood that are connected to each other (Watts 1999). In our context, the clustering coefficient of an idea would be the proportion of existing backers that have other co-backing relationships with each 49 other. 50 4 We transformed this variable, such that the transformed variable is 0 if Affil X CommunalUpdates = 0; and the 51 it-1 it-1 transformed variable is 1 if Affilit-1 X CommunalUpdatesit-1 > 0. Since Affilit-1 X CommunalUpdatesit-1 = 0 for 93.3% 52 of all observations, this transformation does not majorly change the original variable but offers the advantage of 53 reduced multicollinearity. The variance inflation factor of the transformed variable is 1.9. 54 5 Our results are robust to the inclusion of four additional controls: a dummy for whether the funding goal has been 55 met, the number of backers of the focal idea on the focal day, “backer propensity,” a measure of how likely backers 56 are to fund, and “backer longevity,” a measure of how long backers have been active on the platform. 57 58 59 60 Journal of Marketing Page 20 of 80

20 1 2 Author Accepted Manuscript 3 size of the crowd or the number of peers, that would be equivalent to herding, not our study’s 4 5 6 primary focus. In other words, our main objective is not to estimate how backer A will fund the 7 8 focal idea if another backer B has previously funded it. We account for herding in our model by 9 10 incorporating the prior number of backers of the focal idea as a control. Instead, our objective is 11 12 to study the effect of affiliation, which is formed when two backers back an idea, which is not 13 14 15 the focal idea. Affiliation arises in collaborative contexts (e.g., board interlocks, product 16 17 development teams)Peer rather than commonReview product purchases. Version We note that this is a key difference 18 19 of our paper from other contexts. Additionally, our interest is less in modeling agent behavior 20 21 22 (e.g., an individual’s rating of a product in Sunder et al. (2019)) than in modeling product 23 24 success (i.e., funding success of an idea). We now address three main issues that could confound 25 26 identifying the causal effect of affiliation on the focal idea's funding. 27 28 29 Correlated unobservables. Idea-specific characteristics that are not observable to the 30 31 researcher could be correlated with our affiliation measure and affect the focal idea’s funding. 32 33 Perhaps highly affiliated backers are attracted to ideas with high (or low) unobserved quality. 34 35 The inability to control for quality dimensions might induce an upward (or downward) bias in 36 37 38 our estimate of the effect of affiliation. Following Nair et al. (2010) and Sunder et al. (2019), we 39 40 incorporate idea-specific fixed effects. These effectively control for all idea-specific factors that 41 42 might be correlated with affiliation. Next, there could be time-varying factors across the funding 43 44 45 window, which might be correlated with affiliation and funding. For example, affiliation and 46 47 funding are both likely low in the first few days of funding. We control for all day-specific trends 48 49 by incorporating day-fixed effects. Finally, the presence of idea-specific time-varying factors 50 51 52 cannot be ruled out. We control for funding received by the focal idea on day t-1. As mentioned 53 54 55 56 57 58 59 60 Journal of Marketing Page 21 of 80

21 1 2 Author Accepted Manuscript 3 earlier, this approach enables us to control those time-varying idea-specific unobservables, which 4 5 6 may be serially correlated, and to attenuate serial correlation among the residuals. 7 8 Simultaneity. In the context of peer influence, simultaneity implies that not only can the 9 10 influencer influence the focal individual, but the focal individual could also affect the 11 12 influencer’s actions, leading to an upward bias in the estimate of peer effects. In our context, 13 14 15 affiliations formed on a focal day may affect the focal idea’s funding. Simultaneously, the focal 16 17 idea’s funding on a focalPeer day also Review affects affiliation formationVersion on that day. Following recent 18 19 literature (e.g., Park et al. 2018), we use the lagged measures of affiliation in the model. While 20 21 22 affiliation before the focal day can affect funding on the focal day, the reverse is not possible. 23 24 Endogenous group formation (or homophily). Backers with similar preferences may be 25 26 more likely to behave similarly. In such a scenario, the effect of prior affiliation on subsequent 27 28 29 funding of the focal idea might manifest these common preferences. The literature on consumer 30 31 peer effects has used consumer-specific fixed effects to deal with this. However, crowdfunding is 32 33 different from consumer contexts in that while consumers buy (and evaluate) several products, 34 35 backers typically fund very few ideas on a platform. 36 37 38 Moreover, unlike crowdfunding, consumer contexts generally focus on the individual 39 40 more than collective action (Simpson et al. 2021). So, backer-specific fixed effects are 41 42 econometrically infeasible to estimate for both the researcher and the platform. Instead, we first 43 44 45 include the cumulative number of backers, and several other network measures, as controls. 46 47 Next, we note that controlling for lagged funding of the idea also controls backer characteristics 48 49 that have affected funding before the focal day. 50 51 52 Finally, we include an instrument for affiliation. If our measure of affiliation is correlated 53 54 with the error term in equation 1, its coefficient could be biased. In our primary analysis, we use 55 56 57 58 59 60 Journal of Marketing Page 22 of 80

22 1 2 Author Accepted Manuscript 3 an observed instrument to estimate a 2SLS IV regression model. As Rossi (2014, p. 4) mentions, 4 5 6 the ideal solution for endogeneity is to conduct an experiment where the endogenous variable is 7 8 uncorrelated with the construction’s dependent variable. Therefore, we ran controlled 9 10 experiments, which we explain later, where participants were randomly assigned to different 11 12 affiliation levels, creating exogenous variation. 13 14 15 For the primary IV approach, we follow recent research (e.g., Germann et al. 2015; 16 17 Sridhar et al. 2016) thatPeer uses instruments Review based on agent Version behavior in categories (or firms) 18 19 different from the focal category (or a firm). Following this approach, we use the mean (across 20 21 22 ideas) of affiliations on day t-1 of all ideas in our Kickstarter data, which are in a category 23 24 different from that of the focal idea as the primary instrument for Affilit-1 in Kickstarter. For 25 26 example, for an observation about an idea on movies on Dec 22, this instrument is the mean of 27 28 29 affiliations on Dec 22 of all ideas in our data, which are not in the movies category. This 30 31 instrument is correlated with Affilit-1 (correlation = .16). 32 33 Conceptually, this instrument is appealing because of the interdependencies across 34 35 different parts of the global affiliation network on Kickstarter, i.e., the affiliation network across 36 37 38 all ideas seeking funding concurrently, thus satisfying the relevance criterion. However, as most 39 40 backers only back one idea (i.e., affiliation is sparse), the mean affiliation across ideas in other 41 42 categories is very unlikely to be related to the unobserved component of the focal idea’s funding 43 44 45 outcome in equation 1 providing the basis for identification. Further, a category-level measure of 46 47 affiliation should remain unaffected by idea-level factors, especially if the idea is from a 48 49 different category. A category level measure should not correlate strongly with idea-day level 50 51 52 idiosyncratic shocks from another category, thus meeting the exclusion criterion. The first stage 53 54 equation is specified as: 55 56 57 58 59 60 Journal of Marketing Page 23 of 80

23 1 2 Author Accepted Manuscript 3 log(Affil ) =  +  IV +  log(CumBackers ) +  log(CumUpdates ) +  log(y ) + 4 it-1  1 it-1 2 it-1 3 it-1 4 it-1 5 5PropGoalit-1 + 6PropDurationit-1 + 7LastWeekit-1 + 8Networkit-1 + 6 9log(CommunalUpdatesit-1) +it-1 (2) 7 2 8 The R for the first stage regression without the instrument (i.e., assuming 1 = 0) is 0.365 9 10 and with the instrument is 0.385, showing that the instrument’s addition improves the in-sample 11 12 13 model fit. The estimate of 1 is .42 (p < .01). The corresponding F-statistic for the F-test of 14 15 excluded instruments is 879.83, far exceeding the threshold value of 10 (Stock et al. 2002, p. 16 17 Peer Review Version 18 522). The large value of the Anderson-Rubin statistic (F(1, 28300) = 298.41) rejects the null 19 20 hypothesis that the instrument is weak. We show in robustness analyses that the estimates are 21 22 consistent across the use of alternative instruments. We also instrument for the interaction of 23 24 affiliation and the number of communal words contained in the updates (CommunalUpdates ). 25 it-1 26 27 Following Papies et al. (2017), the instrument for this interaction variable is the interaction of the 28 29 instrument for affiliation and CommunalUpdatesit-1. We do not instrument for the interaction of 30 31 affiliation and the number of Facebook shares as the Durbin-Wu-Hausman test of the hypothesis 32 33 2 34 that this regressor is exogenous could not be rejected (χ = 0.055; p > .1). Furthermore, the 35 36 sharing activity of a specific idea on a social media platform other than Kickstarter is 37 38 conceptually independent of its funding outcome on Kickstarter. 39 40 41 Results. First, we present the parameter estimates of the IV regression models estimated 42 43 on the Kickstarter data and then discuss robustness checks. 44 45 We present estimates of five models, with and without instruments, and the sequential 46 47 addition of interactions in Table 5. M1 to M4 do not have interaction effects, and while M1 48 49 50 ignores endogeneity, M2, M3, and M4 correct for it and show that the results are robust to 51 52 different instruments. The results from the full model specified in equation 1 are reported in M5, 53 54 which we next discuss. 55 56 57 58 59 60 Journal of Marketing Page 24 of 80

24 1 2 Author Accepted Manuscript 3 Full Model Results. We find that affiliation among backers has a consistent negative 4 5 6 effect on the funding of ideas on Kickstarter (훽 = -.87, p < .01). This effect persists despite the 7 8 inclusion of idea-specific fixed effects, daily fixed effects, controlling for lagged funding, and 9 10 the prior number of backers of the idea. We corroborate extant findings on herding (e.g., Zhang 11 12 13 and Liu 2012) and additionally show that affiliation plays a key role and that its effect is 14 15 negative. 16 17 Peer Review-- Insert Table 5 about Version here -- 18 19 Concerning the moderators, the creator’s engagement measured as using communal 20 21 22 words in updates further strengthens the negative effect of affiliation, perhaps because of a 23 24 heightened licensing effect (훽 = -7.68, p < .01). For backer engagement, we find the negative 25 26 effect of affiliation is stronger as backer engagement, measured as the number of Facebook 27 28 29 shares of the idea by backers, increases (훽 = -.006, p < .01). One explanation of this is that while 30 31 individuals share on Facebook for various motives, the primary motivation is prosocial, and 32 33 others seeing the shares likely see them as such, strengthening the vicarious moral licensing 34 35 36 effect (Kim and Yang 2017). 37 38 Concerning control variables, the greater the number of backers of an idea before the 39 40 focal day, a measure of herding, the more funding the idea will attract on the focal day ( = 2.02, 41 42 43 p < .01). This indicates that the total number of backers for an idea may act as a signal of its 44 45 quality or potential worthiness, a finding that is consistent with prior research (e.g., Lin et al. 46 47 2013). The current research replicates this effect and demonstrates that social structure 48 49 50 influences behavior beyond the herding effect. Moreover, this theory supports our contention that 51 52 affiliation, measured by co-backing, drives the negative effect, not herding. We also find that the 53 54 total number of updates posted by the creator has a negative effect on crowdfunding success (훽 = 55 56 57 58 59 60 Journal of Marketing Page 25 of 80

25 1 2 Author Accepted Manuscript 3 -.05, p < .10), although this effect is not significant across all model specifications. The effect of 4 5 6 the proportion of the funding goal which was achieved on the previous day is negative (훽 = -.17, 7 8 p < .05), perhaps suggesting a preference to fund underfunded ideas. For network centrality 9 10 measures, we find that betweenness (β = -.02, p < .05) and eigenvector centrality (β = -4.40, p < 11 12 13 .05) have a negative effect on funding. The negative effects of these second-order network 14 15 measures, compared to the positive effect of number of backers (proxy for first order network 16 17 effect), highlight thePeer complexity Reviewin flow of information Version on the network and are consistent with 18 19 findings from prior studies (e.g., Mallapragada et al. 2012). This is perhaps because these 20 21 22 measures indicate the backers’ ability to identify and fund salient opportunities, or access to 23 24 information from their overall networks about idea quality based on indirect ties across the whole 25 26 network, not just direct ties. Thus, the effects also highlight the importance of distinguishing 27 28 29 direct and indirect aspects of how networks operate in community contexts. 30 31 To ensure that outliers are not driving our results, we estimate the main model (M5) after 32 33 dropping the top 10 percentile of observations (which have affiliation values greater than 7, 34 35 36 yielding a significant and negative estimate of the affiliation coefficient (훽 = -.87, p < .01). We 37 38 find a similar negative effect in models estimated on various subsets of the data. To investigate if 39 40 specific categories of ideas drive our results, we estimate the model separately for each 41 42 category’s ideas. We find a negative effect of affiliation for 11 out of 12 categories, with the 43 44 45 most negative effect of affiliation in the ideas from the photography and technology categories. 46 47 Our estimate of affiliation’s effect is negative but not statistically significant for the “dance” 48 49 category, which accounts for just 21 out of 2,021 ideas in our data. 50 51 52 Robustness analyses. We conducted several robustness analyses. First, we estimated the 53 54 model on Indiegogo data; the results are quite consistent (see Web Appendix F). Second, we 55 56 57 58 59 60 Journal of Marketing Page 26 of 80

26 1 2 Author Accepted Manuscript 3 estimated the model on Kickstarter data using three alternative sets of instruments: discrete latent 4 5 6 instrumental variables, an instrument constructed using affiliation from another platform, and a 7 8 network-based instrument (see Web Appendix G). Third, we estimated Probit, Logit, and Tobit 9 10 models of funding success and checked the robustness of our results to two alternative measures 11 12 of affiliation (Web Appendix H). All analyses show that our results are robust. 13 14 15 Next, we report three experiments where we probe the effect of affiliation, the underlying 16 17 process, and a moderatingPeer factor Reviewto further validate ourVersion empirical model. 18 19 Collection and Analysis of Experimental Data 20 21 22 In the first experiment, we demonstrate the negative effect of affiliation in a controlled 23 24 experimental setting. In the second experiment, we validate vicarious moral licensing as an 25 26 underlying mechanism and rule out uniqueness as one potential alternative explanation. In the 27 28 29 third experiment, we show how the idea’s description might moderate the effect of affiliation. 30 31 Experiment 1. We conducted the study on Amazon Turk with 200 North American 32 33 6 residents (M Age = 35.26 years, 49.8% women; 42.6% of whom have previously funded a 34 35 crowdfunding idea). We presented participants with two ideas seeking funding (both real ideas 36 37 38 from Kickstarter, see Web Appendix I). First, participants saw a screenshot of a website created 39 40 by a graphic designer to look like an idea page on a real crowdfunding platform (e.g., Burtch et 41 42 al. 2013; Younkin and Kuppuswamy 2017). 43 44 45 Consistent with prior research, participants were given money beyond study payment, 46 47 creating an incentive-compatible dependent measure (Goenka and Van Osselaer 2019; 48 49 Morewedge et al. 2019). Participants were told, “As part of this study, you will receive a $2 50 51 52 53 54 6 Because this study involved real pay, we restricted participants to not include those who had completed over 1500 55 studies as these “professional” Amazon Turkers may behave in significantly different ways, particularly when 56 monetary bonuses are included (Wessling et al. 2017). 57 58 59 60 Journal of Marketing Page 27 of 80

27 1 2 Author Accepted Manuscript 3 bonus. You can use some or all of this money to fund this project.” They were then asked how 4 5 6 much they would give toward the idea on a 9-point scale with dollar amounts in $.25 intervals, 7 8 ranging from $0 to $2.00. If participants chose “$0” and opted to keep the full bonus, they were 9 10 then forwarded to the end of the survey and were paid the original Amazon Turk fee as well as 11 12 the $2 bonus. If participants used any of their bonus to fund the first idea, they were included in 13 14 15 our primary analyses. Ninety-three participants opted not to fund the first idea leaving us with 16 17 107 participants. FourPeer participants Review were removed who Version indicated that they had a child affected by 18 19 autism, the focus of one of the two ideas, and were inclined toward funding but would opt to put 20 21 22 the money toward helping their child. All participants completed the dependent measures. Two 23 24 participants were removed for spending less than a second on the manipulation, leaving us with 25 26 101 participants. Then, they saw a screenshot of a second website designed to look like an idea 27 28 29 on a crowdfunding platform; see Web Appendix I for details. 30 31 The screenshot included idea information and a list of recent backers shown on the 32 33 screen’s right side. Participants in the high affiliation condition saw a high overlap in the number 34 35 of backers across the two ideas. Participants in the control affiliation condition saw the same 36 37 38 number of backers, but the names on the two lists did not overlap. A manipulation check 39 40 confirmed the effectiveness of the manipulation. All participants who funded the first idea were 41 42 told that they would receive an additional $2 bonus to keep or use to fund the second idea. Their 43 44 45 decision on a 9-point scale ranging from $0 to $2.00 in $.25 intervals served as the outcome. At 46 47 the end of the study, participants were given the money that they chose to keep as a bonus, and 48 49 the remainder (i.e., what they chose to fund each of the ideas) was put toward each crowdfunding 50 51 52 idea. Finally, participants responded to a set of demographic measures (e.g., age, gender, and 53 54 whether they had previously funded an idea on a crowdfunding platform). A one-way ANOVA 55 56 57 58 59 60 Journal of Marketing Page 28 of 80

28 1 2 Author Accepted Manuscript 3 showed a significant effect of affiliation on the funding of the second idea (F(1, 99) = 4.05, p < 4 5 6 .05). As expected, those in the high affiliation condition funded less than those in the control 7 8 affiliation condition (Mhigh = 4.27, SD = 2.27 vs. Mcontrol = 5.27, SD = 2.70). Of the $2 bonus, 9 10 those in the high affiliation chose to fund $.82 toward the focal idea, while those in the control 11 12 affiliation condition chose to fund $1.07, on average. 13 14 15 The first experiment confirmed the negative effect of affiliation in the lab setting, 16 17 validating our primaryPeer empirical Reviewfinding that affiliation Version negatively affects crowdfunding success. 18 19 Experiment 2. In the second experiment, we measured two potential mediators in an 20 21 22 attempt to document “a” mediating process (i.e., the mediating process given our stimuli and 23 24 procedures) as opposed to “the” mediating process (i.e., a single mediating process that is 25 26 operative across all crowdfunding contexts; e.g., Buechel and Janiszewski 2013). We propose 27 28 29 vicarious moral licensing as a mechanism for the negative impact of affiliation on funding and 30 31 test need for uniqueness as an alternative mechanism (Tian et al. 2001). 32 33 We conducted the study on Amazon Turk with 228 North American residents (M Age = 34 35 39.57 years, 54.4% women; 38.2% of whom have previously funded an idea on an online 36 37 38 crowdfunding platform). All participants spent adequate time on the manipulation. Three 39 40 participants did not complete the dependent measures, resulting in an effective sample of 225 41 42 participants. Participants were told to imagine that they had $50 and were asked to choose one 43 44 45 idea to fund from a set of four real ideas seeking funding on Kickstarter and across categories 46 47 (e.g., technology, non-profits, and arts/film); details appear in Web Appendix I. After this 48 49 decision, they read about a second idea that they were told is seeking funding. Those in the high 50 51 52 affiliation condition were told that many of the backers who funded the first idea they chose also 53 54 funded the focal idea. Those in the control affiliation condition were provided no information 55 56 57 58 59 60 Journal of Marketing Page 29 of 80

29 1 2 Author Accepted Manuscript 3 about other backers’ funding decisions; a pretest confirmed the effectiveness of the manipulation 4 5 6 (see Web Appendix I). Next, participants responded to two items to capture vicarious moral 7 8 licensing (“Based on the funding behavior of co-backers, I do not feel the need to fund [focal 9 10 idea]” and “Based on the funding behavior of co-backers, I do not feel obligated to fund [focal 11 12 idea]”; M = 4.10, SD = 1.48; r = .72) and two items to capture uniqueness (“If I funded [focal 13 14 15 idea], my decision to fund would say a lot about me as a unique individual” and “If I funded 16 17 [focal idea], it wouldPeer help me stand Review out from the crowd”; Version M = 3.68, SD = 1.45; r = .81). 18 19 Next, we asked participants how much money they would pledge toward funding the 20 21 22 subsequent focal idea (range: $0 - $5,000, the total needed to hit the focal idea’s funding goal). 23 24 Consistent with prior research and our empirical model, we log-transformed funding (Matthews 25 26 et al. 2016). Finally, participants completed demographic questions. 27 28 29 As expected, we found a negative effect of affiliation on funding (F(1, 223) = 4.29, p < 30 31 .04) such that those in the high affiliation condition reported a lower funding amount than those 32 33 in the control condition (Mhigh = 3.17, SD = 2.49 vs. Mcontrol = 3.82, SD = 2.24) or in raw numbers 34 35 (M = $256.96, SD = $674.40 vs. M = $339.88, SD = $875.90). A one-way ANOVA 36 high control 37 38 showed a significant effect of affiliation on the licensing measure (F(1, 223) = 3.89, p = .05). As 39 40 expected, those in the high affiliation condition agreed more with the licensing measure, 41 42 indicating less need to fund than those in the control condition (M = 4.29, SD = 1.57 vs. 43 high 44 45 Mcontrol = 3.90, SD = 1.37). However, there was no significant effect of affiliation on uniqueness 46 47 (Mhigh = 3.56, SD = 1.52 vs. Mcontrol = 3.80, SD = 1.36; F(1, 223) = 1.53, p = .22). We then 48 49 assessed the indirect effects of the two mediators on funding. The results indicate that licensing 50 51 52 was a significant mediator (95% CI does not include 0: -.4423, - .0003), but uniqueness was not 53 54 (95% CI: -.5479, .1142). 55 56 57 58 59 60 Journal of Marketing Page 30 of 80

30 1 2 Author Accepted Manuscript 3 In this experiment, we replicated the negative effect of affiliation and uncovered 4 5 6 vicarious moral licensing as an underlying mechanism. While we did not find an effect of 7 8 affiliation on uniqueness in this study, we note that uniqueness may operate more strongly for 9 10 some ideas and some individuals, providing an interesting avenue for future research on 11 12 crowdfunding (Tian et al. 2001). 13 14 15 Experiment 3. In this experiment, we explored the role of a moderator: how the creator 16 17 describes the idea. WePeer theorized Reviewthat the negative effect Version of affiliation occurs in a crowdfunding 18 19 context, at least partly due to its communal nature and how the ideas are presented to potential 20 21 22 backers. We conducted the third experiment on Amazon Turk with 206 North American 23 24 residents (M Age = 38.81 years, 46.1% women; 42.2% of whom have previously funded an idea 25 26 on an online crowdfunding platform). All participants completed the dependent measures. Three 27 28 29 participants who spent less than one second reading the manipulation were removed, resulting in 30 31 N = 203. We manipulated two factors between participants: (1) affiliation (high vs. control) and 32 33 (2) idea description (more vs. less communal). 34 35 As in experiment 2, participants read about an idea currently seeking funding on 36 37 38 Kickstarter and were told to imagine that they had funded this idea (see Web Appendix I). We 39 40 used the same manipulation of affiliation as in experiment 2. Those in the high affiliation 41 42 condition were told that many backers who funded the first idea they chose also funded the focal 43 44 45 idea. Those in the control affiliation condition were not provided any information about other 46 47 backers’ funding decisions. Participants then read about diveLIVE, a technology that allows 48 49 divers to talk underwater while streaming live video to the internet. diveLIVE, the focal idea, 50 51 52 was described as more or less communal with small changes (e.g., “Let’s learn about the oceans” 53 54 vs. “This product uses technology to take videos of the oceans”). 55 56 57 58 59 60 Journal of Marketing Page 31 of 80

31 1 2 Author Accepted Manuscript 3 Next, participants indicated how much money they would pledge toward diveLIVE, the 4 5 6 focal idea (range: $0 - $20,000, the total needed to hit the focal idea’s funding goal). Consistent 7 8 with prior research, our empirical model, and experiment 2, we log-transformed funding 9 10 (Matthews et al. 2016) for analysis but provide results in raw numbers for ease of interpretation. 11 12 Finally, participants completed demographic questions. 13 14 15 We found evidence for a main effect of idea description (F(1, 199) = 13.86, p < .01) 16 17 consistent with priorPeer research, which Review finds that ideas Versiondescribed as more communal tend to be 18 19 more successful than those which are described as an investment opportunity (Allison et al. 20 21 22 2015). More importantly, we found an interaction between the two manipulated factors (F(1, 23 24 199) = 5.84, p < .02). As expected, when the idea was described as more communal, those in the 25 26 high affiliation condition reported lower funding than those in the control affiliation condition 27 28 29 (Mhigh = $2155.32, SD = $3998.08 vs. Mcontrol = $4073.04, SD = $5316.08; t(199) = 2.09, p < 30 31 .04). When the idea was described as less communal, there was no effect of affiliation on funding 32 33 (Mhigh = $2572.33, SD = $4933.01 vs. Mcontrol = $1868.68, SD = $4039.03; t(199) = -1.34, p = 34 35 .18; see Figure W9 in Web Appendix I). 36 37 38 The third experiment established that the negative effect of affiliation is stronger when 39 40 creator’s use more communal words in the description of the idea. 41 42 Validating Moderation with Observational Data 43 44 45 As discussed earlier, we find a negative moderating effect of the number of communal 46 47 words in updates posted by creators. To validate the third experiment with converging evidence, 48 49 we returned to our secondary data to examine how the number of communal words in the idea 50 51 52 description influenced the relationship between affiliation and funding behavior across thousands 53 54 of crowdfunding ideas (e.g., Netzer et al. 2019). This would establish how the use of communal 55 56 57 58 59 60 Journal of Marketing Page 32 of 80

32 1 2 Author Accepted Manuscript 3 words in creator’s updates as well as in the idea’s description would influence the effect of 4 5 6 backer affiliation and highlight the importance of the communal mechanism. We used the same 7 8 text dictionary that we created for coding communal words in updates and coded the description 9 10 of every idea in our sample. The median number of communal words in an idea description is 3 11 12 (mean is 6.1). We then created two subsets of our data based on a median split of the number of 13 14 15 communal words used in describing the idea. We estimated the model separately on each subset 16 17 and find that the coefficientPeer of affiliation Review is less negative Version for ideas described using three or fewer 18 19 communal words (M = -.92, SE = .09) than for ideas described using four or more communal 20 21 22 words (M = -1.25, SE = .15). Replacing the number of communal words in this analysis with the 23 24 ratio of the number of communal words to the total number of words does not affect this result. 25 26 Splitting the data based on the average number of communal words instead of the median does 27 28 29 not affect the result. Finally, the effect of affiliation is less negative for ideas with no communal 30 31 words than for ideas with at least one communal word. This provides real-world evidence for the 32 33 role of idea description on the relationship between affiliation and funding behavior, validating 34 35 our theory and experimental evidence. 36 37 38 In summary, these findings further validate our reasoning that the negative effect of 39 40 affiliation is driven, at least in part, by the communal nature of crowdfunding and the prosocial 41 42 mindset that it prompts (Simpson et al. 2021). When an idea is described as more communal, 43 44 45 these prosocial goals are exacerbated, leading potential backers to feel that they do not need to 46 47 fund because these affiliated others are funding the idea (e.g., Meijers et al. 2019). However, 48 49 when an idea is described as less communal, this effect is mitigated. Next, we discuss our results 50 51 52 and develop implications for theory and practice. 53 54 55 56 57 58 59 60 Journal of Marketing Page 33 of 80

33 1 2 Author Accepted Manuscript 3 Discussion 4 5 6 We establish a negative effect of affiliation on the crowdfunding success of ideas using a 7 8 large empirical study and then validating the effect through experiments. We provide preliminary 9 10 insights into the role of vicarious moral licensing as the underlying mechanism for this effect and 11 12 investigate the moderating role of creator and backer engagement. The licensing effect and its 13 14 15 role in reducing backers’ perceived obligation to fund ideas could make backers less likely to 16 17 fund or fund with lessPeer money if theyReview opt to fund, both Version of which could explain the negative effect 18 19 at the idea level. We begin with a focus on the novel contribution of our finding concerning 20 21 22 affiliation, discuss the economic implications of our results, and identify the primary 23 24 contributions of our research and how it paves the way for future research. 25 26 The negative effect of affiliation among backers in crowdfunding is distinct from and in 27 28 29 addition to the positive effect of herding due to the crowd’s size shown in past research (e.g., 30 31 Zhang and Liu 2012). We establish an inherent tension between the positive effect of crowd size 32 33 and the negative effect of backer affiliation in crowdfunding. Thus, we show that, in addition to 34 35 relying on crowd size, backers make inferences based on the behavior of affiliated others in a 36 37 38 crowdfunding context. A 10% daily increase in number of backers leads to an additional 20.2% 39 40 in funding or an increase of 83 USD/day (i.e., the herding effect). In contrast, a 10% daily 41 42 increase in backer affiliation leads to an 8.7% decrease in funding or a decrease of 36 USD/day, 43 44 45 offsetting the increase due to number of backers by 43%. Our results concerning affiliation are 46 47 both statistically and economically meaningful and highlight the need to recognize the tension 48 49 between increasing the number of backers and limiting the ill effects of affiliation. 50 51 52 Interestingly, Kickstarter stopped disclosing the prior backers’ list on an idea’s page as of 53 54 the time of writing the paper. This policy change is consistent with our results. If backer 55 56 57 58 59 60 Journal of Marketing Page 34 of 80

34 1 2 Author Accepted Manuscript 3 identities remain unknown, potential backers cannot infer affiliation, and therefore ideas cannot 4 5 6 be negatively impacted by backer affiliation. Other crowdfunding platforms should re-evaluate 7 8 disclosure policies about past backers of an idea or perhaps reconsider whom they show at the 9 10 top of their backer lists. 11 12 So how might creators mitigate the negative effects of affiliation? The moderation effects 13 14 15 from our results provide actionable insights for creators seeking crowdfunding from potential 16 17 backers and consideringPeer what platforms Review to pursue. Our Version results concerning the interaction 18 19 between affiliation and creator engagement show that creators can subdue the negative effects of 20 21 22 affiliation by carefully crafting the idea description and updates, avoiding communal language. 23 24 Further, while it appears that encouraging backers to share the idea on social media might 25 26 be counterproductive because it strengthens affiliation’s negative effect, the impact is small and 27 28 29 should not be a major concern. The change in the marginal effect of affiliation as sharing by 30 31 backers increases is small, indicating that change in backers’ engagement, while statistically 32 33 significant, does not have a meaningful effect on crowdfunding. Doubling the number of 34 35 Facebook shares, from its mean of 79 to 148, strengthens the negative effect of affiliation by 36 37 38 0.42% and translates to a decline of 1.72 USD/day. 39 40 We developed recommendations for creators and examples of best practices from our 41 42 dataset (see Table 6). For example, creators should focus on the idea’s inherent purpose and 43 44 45 objective value in its description and avoid using too much communal language (e.g., cooperate, 46 47 partner, support) in the idea description and updates. Overall, we recommend that platforms 48 49 educate creators on how best to structure communication with backers and guide creators in 50 51 52 meeting their goals. Backers could perhaps learn to interpret such updates better and use the 53 54 information provided by the backer to qualify what they infer from the community. 55 56 57 58 59 60 Journal of Marketing Page 35 of 80

35 1 2 Author Accepted Manuscript 3 -- Insert Table 6 about here -- 4 5 6 Our results about the mechanism provide insights on how platforms and creators should 7 8 engage with backers. Research has shown that licensing is a non-conscious effect and can be 9 10 mitigated by making individuals aware of their behavior (Khan and Dhar 2006). Particularly in 11 12 this type of vicarious moral licensing, highlighting individuals’ uniqueness and independent 13 14 15 identity may also mitigate the negative effect of affiliation on funding (Kouchaki 2011; Meijers 16 17 et al. 2019; NewmanPeer and Brucks Review2018). If creators expect Version high overlap among backers, they 18 19 could describe their ideas using less communal language, thereby lowering the licensing effect. 20 21 22 Our results suggest vicarious licensing might overwhelm other relevant idea information, 23 24 potentially leading to suboptimal backer decisions. Based on our findings, backers might, in 25 26 some cases, pay more attention to signals from affiliated others compared to the whole crowd. 27 28 29 For crowdfunding platforms, our findings provide a rationale for why there might be 30 31 room for new crowdfunding platforms to thrive and grow. Although several crowdfunding 32 33 platforms have flourished in the past decade, Kickstarter, Indiegogo, and GoFundMe have 34 35 arguably dominated the market. Others such as Sellaband and PledgeMusic, which were once 36 37 38 popular, have failed. Large platforms with millions of backers might pose high entry barriers to 39 40 new entrants. However, our findings point to one source of competitive advantage for newer 41 42 platforms: negative affiliation effects are more likely in well-established platforms with large 43 44 45 backer communities. Strategically building diverse and unaffiliated communities of backers 46 47 might confer a competitive advantage to new platforms. Our results show that this can be 48 49 achieved by expanding the number of categories of ideas as affiliation’s negative effect may be 50 51 52 mitigated as backers of ideas across different categories may be less likely to co-back ideas. The 53 54 failure of category-specific platforms such as Sellaband (music), and the relative success of 55 56 57 58 59 60 Journal of Marketing Page 36 of 80

36 1 2 Author Accepted Manuscript 3 platforms hosting diverse ideas, such as Kickstarter, provides support for this reasoning. Second, 4 5 6 platforms allocating marketing resources across existing and new backers (e.g., allocating social 7 8 media spending across established markets such as Los Angeles and new markets such as Lima) 9 10 could perhaps view our results as a reason to divert resources away from backer-dense markets. 11 12 Third, platforms that provide backer information may also want to use algorithms that promote 13 14 15 unaffiliated (vs. affiliated) backers, for example, by highlighting first-time backers. Finally, 16 17 based on our results Peerabout creator Review engagement, we recommend Version that platforms educate creators on 18 19 how to design better backer communication. 20 21 22 Insights from our study are relevant to other types of crowdsourcing platforms as well. 23 24 For example, participants on LEGO’s Ideas, which focuses on ideation, and SeedInvest, which 25 26 helps raise equity, could mitigate the ill-effects of affiliation, for example, by describing 27 28 29 initiatives as less communal and by posting updates with less communal language. Our findings 30 31 are also applicable to crowdfunding contests (e.g., Camacho et al. 2019; Hurst 2017), where 32 33 participants could be encouraged to vote across categories to reduce co-participation and help 34 35 them break away from the adverse effects of groupthink. 36 37 38 We highlight several areas of inquiry for future research. Reward structures could impact 39 40 the role of affiliation in crowdfunding and hence merit attention (e.g., Sun et al. 2017). Fake 41 42 reviews have been investigated in the online context (e.g., Zhao et al. 2013) and it would be 43 44 45 interesting to explore the veracity of idea descriptions and creator updates. In addition to 46 47 affiliation, which we study, other network characteristics such as clans and core-periphery 48 49 structures (Wasserman and Faust 1999) could explain the nature of information flow across 50 51 52 affiliation structures. 53 54 55 56 57 58 59 60 Journal of Marketing Page 37 of 80

37 1 2 Author Accepted Manuscript 3 As interest in crowdfunding continues to increase, interesting research questions continue 4 5 6 to emerge. We believe that our research explores important questions concerning crowdfunding 7 8 that involve backer affiliation and community structure, and we hope to lay the foundation for 9 10 future studies in the domain. 11 12 13 14 15 16 17 Peer Review Version 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 38 of 80

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40 1 2 Author Accepted Manuscript 3 Kouchaki, Maryam (2011), "Vicarious Moral Licensing: The Influence of Others' Past Moral Actions on 4 Moral Behavior," Journal of Personality and Social Psychology, 101 (4), 702-15. 5 6 Kuppuswamy, Venkat and Barry L. Bayus (2017), "Does My Contribution To Your Crowdfunding 7 Project Matter?," Journal of Business Venturing, 32 (1), 72-89. 8 9 10 Leary, Mark R. (2010), "Affiliation, Acceptance, and Belonging: The Pursuit of Interpersonal 11 Connection," in Handbook of Social Psychology. 5th ed. Vol. 2. Hoboken, NJ, US: John Wiley & Sons, 12 Inc. 13 14 Li, Gen and Jing Wang (2019), "Threshold Effects on Backer Motivations in Reward-based 15 Crowdfunding," Journal of Management Information Systems, 36 (2), 546-73. 16 17 Lin, Mingfeng, NagpurnanandPeer R. Prabhala, Review and Siva Viswanathan Version (2013), "Judging Borrowers by the 18 Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer 19 Lending," Management Science, 59 (1), 17-35. 20 21 Mallapragada, Girish, Rajdeep Grewal, and Gary Lilien (2012), "User-Generated Open Source Products: 22 Founder's Social Capital and Time to Product Release," Marketing Science, 31 (3), 474-92. 23 24 Matthews, William John, Ana I. Gheorghiu, and Mitchell J. Callan (2016), "Why Do We Overestimate 25 Others’ Willingness to Pay?," Judgment and Decision Making, 11 (1), 21-39. 26 27 Meijers, Marijn H. C., Marret K. Noordewier, Peeter W. J. Verlegh, Simon Zebregs, and Edith G. Smit 28 (2019), "Taking Close Others’ Environmental Behavior Into Account When Striking the Moral Balance? 29 30 Evidence for Vicarious Licensing, Not for Vicarious Cleansing," Environment and Behavior, 51 (9-10), 31 1027-54. 32 33 Moradi, Masoud and Mayukh Dass (2019), "An Investigation into the Effects of Message Framing on 34 Crowdfunding Funding Level," Journal of Electronic Commerce Research, 20 (4), 238-54. 35 36 Morewedge, Carey K., Meng Zhu, and Eva C. Buechel (2019), "Hedonic Contrast Effects are Larger 37 When Comparisons are Social," Journal of Consumer Research, 46 (2), 286-306. 38 39 Nair, Harikesh S., Puneet Manchanda, and Tulikaa Bhatia (2010), "Asymmetric Social Interactions in 40 Physician Prescription Behavior: The Role of Opinion Leaders," Journal of Marketing Research, 47 (5), 41 883-95. 42 43 Narayan, Vishal and Vrinda Kadiyali (2016), "Repeated Interactions and Improved Outcomes: An 44 Empirical Analysis of Movie Production in the U.S.," Management Science, 62 (2), 591-607. 45 46 Netzer, Oded, Alain Lemaire, and Michal Herzenstein (2019), "When Words Sweat: Identifying Signals 47 for Loan Default in the Text of Loan Applications," Journal of Marketing Research, 56 (6), 960-80. 48 49 50 Newman, Kevin P. and Merrie Brucks (2018), "The Influence of Corporate Social Responsibility Efforts 51 on the Moral Behavior of High Self-Brand Overlap Consumers," Journal of Consumer Psychology, 28 52 (2), 253-71. 53 54 Papies, Dominik, Peter Ebbes, and Harald J. Van Heerde (2017), "Addressing Endogeneity in Marketing 55 Models," in Advanced Methods for Modeling Markets: Springer. 56 57 58 59 60 Journal of Marketing Page 41 of 80

41 1 2 Author Accepted Manuscript 3 Park, Eunho, Rishika Rishika, Ramkumar Janakiraman, Mark B. Houston, and Byungjoon Yoo (2018), 4 "Social Dollars in Online Communities: The Effect of Product, User, and Network Characteristics," 5 Journal of Marketing, 82 (1), 93-114. 6 7 Pennebaker, James W., Ryan L. Boyd, Kayla Jordan, and Kate Blackburn (2015), "The Development and 8 Psychometric Properties of LIWC2015," University of Texas at Austin. 9 10 11 Pietraszkiewicz, Agnieszka, Birthe Soppe, and Magdalena Formanowicz (2017), "Go Pro Bono," Social 12 Psychology. 13 14 Ransbotham, Sam, Gerald C. Kane, and Nicholas H. Lurie (2012), "Network Characteristics and the 15 Value of Collaborative User-generated Content," Marketing Science, 31 (3), 387-405. 16 17 Rossi, Peter E. (2014),Peer "Even the Rich Review can Make Themselves Version Poor: A Critical Examination of IV 18 Methods in Marketing Applications," Marketing Science, 33 (5), 655-72. 19 20 Simpson, Bonnie, Martin Schreier, Sally Bitterl, and Katherine White (2021), "Making the World a Better 21 Place: How Crowdfunding Increases Consumer Demand for Social-Good Products," Journal of 22 Marketing Research, 58 (2), 363-76. 23 24 Sridhar, Shrihari, Frank Germann, Charles Kang, and Rajdeep Grewal (2016), "Relating Online, 25 Regional, and National Advertising to Firm Value," Journal of Marketing, 80 (4), 39-55. 26 27 Srinivasan, Raji, Stefan Wuyts, and Girish Mallapragada (2018), "Corporate Board Interlocks and New 28 Product Introductions," Journal of Marketing, 82 (1), 132-48. 29 30 31 Stalder, Hannah and Sarah-Jane Stenson (2016),"Unilever Foundry And Indiegogo Launch Crowdfunding 32 Partnership To Accelerate Innovation,"(accessed Ocotober 20, 2020) 33 https://www.unileverusa.com/news/press-releases/2016/unilever-foundry-and-indiegogo-launch- 34 crowdfunding-partnership.html 35 36 Statista (2021), "Crowdfunding - Statistics & Facts," (accessed May 5, 2021), 37 https://www.statista.com/topics/1283/crowdfunding/. 38 39 Stock, James H., Jonathan H. Wright, and Motohiro Yogo (2002), "A Survey of Weak Instruments and 40 Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, 20 41 (4), 518-29. 42 43 Sun, Yacheng, Xiaojing Dong, and Shelby McIntyre (2017), "Motivation of User-generated Content: 44 Social Connectedness Moderates the Effects of Monetary Rewards," Marketing Science, 36 (3), 329-37. 45 46 Sunder, Sarang, Kihyun Hannah Kim, and Eric A. Yorkston (2019), "What Drives Herding Behavior in 47 Online Ratings? The Role of Rater Experience, Product Portfolio, and Diverging Opinions," Journal of 48 49 Marketing, 83 (6), 93-112. 50 51 Swaminathan, Vanitha and Christine Moorman (2009), "Marketing Alliances, Firm Networks, and Firm 52 Value Creation," Journal of Marketing, 73 (5), 52-69. 53 54 Tian, Kelly T., William O. Bearden, and Gary L. Hunter (2001), "Consumers' Need for Uniqueness: Scale 55 Development and Validation," Journal of Consumer Research, 28 (1), 50-66. 56 57 58 59 60 Journal of Marketing Page 42 of 80

42 1 2 Author Accepted Manuscript 3 Wasserman, Stanley and Katherine Faust (1999), Social Network Analysis: Methods and Applications. 4 Cambridge, UK: Cambridge University Press. 5 6 Watts, Duncan J. (1999), "Networks, Dynamics, and the Small-world Phenomenon," American Journal of 7 sociology, 105 (2), 493-527. 8 9 10 Wei, Yanhao “Max”, Jihoon Hong, and Gerard J. Tellis (2021), "Machine Learning for Creativity: Using 11 Similarity Networks to Design Better Crowdfunding Projects," Journal of Marketing, Forthcoming. 12 13 Wessling, Kathryn Sharpe, Joel Huber, and Oded Netzer (2017), "MTurk Character Misrepresentation: 14 Assessment and Solutions," Journal of Consumer Research, 44 (1), 211-30. 15 16 Xiang, Diandian, Leinan Zhang, Qiuyan Tao, Yonggui Wang, and Shuang Ma (2019), "Informational or 17 Emotional Appeals inPeer Crowdfunding Review Message Strategy: AnVersion Empirical Investigation of Backers’ Support 18 Decisions," Journal of the Academy of Marketing Science, 47 (6), 1046-63. 19 20 Younkin, Peter and Venkat Kuppuswamy (2017), "The Colorblind Crowd? Founder Race and 21 Performance in Crowdfunding," Management Science, 64 (7), 3269-87. 22 23 Zhang, Juanjuan and Peng Liu (2012), "Rational Herding in Microloan Markets," Management Science, 24 58 (5), 892-912. 25 26 Zhao, Yi, Sha Yang, Vishal Narayan, and Ying Zhao (2013), "Modeling Consumer Learning From Online 27 Product Reviews," Marketing Science, 32 (1), 153-69. 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 43 of 80

43 1 Author Accepted Manuscript 2 3 Table 1. Comparison with Relevant Empirical Research 4 5 References Dependent Explanatory Empirical Model Data Experiments Findings 6 (Published) Variables Variables Features Context 7 Our Research Funding - Affiliation - FE Log-Linear Regression Crowdfunding Yes; 3 Prior backer affiliation decreases funding, negative 8 - Mediator: Vicarious - Endogeneity (Instrument) Kickstarter experiments effect due to vicarious moral licensing; effect 9 moral licensing - Robustness checks: Probit, Experiments stronger for ideas with communal descriptions and 10 - Moderators: creator & Logit, and Tobit more communal updates and more backer sharing 11 backer engagement on social media. Wei, Hong and Success of - Similarity between ideas - Network Similarity Crowdfunding No Prior success of similar ideas affects success, 12 Tellis (2021) funding Peer Review- Binary regression KickstarterVersionfunding performance increases as being novel and 13 imitative are balanced, optimal funding level closer 14 to level of similar ideas. 15 Netzer, Lemaire and Loan payback - Loan descriptions - Text Analytics Crowdlending No Borrowers who use certain types of words are 16 Herzenstein (2019) - Binary regression Prosper more likely to default. 17 Dai and Zhang Funding time - Going past deadline - Regression continuity Crowdfunding No Backers might be driven by prosocial motives 18 (2019) elapsed - Prosocial motivation Kickstarter around deadline following goal pursuit. 19 - Creator is individual 20 Chul et al. (2019) Goal - Forward-looking - Bayesian IJC Crowdfunding No Forward-looking investment behavior, 21 Completion - Social interactions - Two-step (Music) contemporaneous and forward-looking social - Counterfactuals Sellaband interactions impact share purchases and goal 22 completion. 23 Burtch et al. (2016) Contributions - Concealment - Tobit Crowdfunding No Concealment hurts the likelihood of contribution 24 - Social norms - Endogeneity (IV) Dataset and contribution. Social norms drive concealment. 25 undisclosed 26 Agrawal et al. Decision to - Geography - Linear regression Crowdfunding No Local backers are not influenced by artist. Effect 27 (2015) invest - Fixed Effects (Music) does not persist past the first investment, indicates 28 Sellaband the role of search but not monitoring. 29 30 Burtch et al. (2013) Contribution - Crowding - Log-linear regression Crowdfunding No Partial crowding-out effect, backers experience the Frequency - Funding window - Endogeneity (GMM) (Journalism) lower marginal utility of giving as the funds 31 - Degree of exposure Dataset become less relevant to the recipient. The funding 32 undisclosed window and degree of exposure have a positive 33 effect, post-publication of the story. 34 Lin et al. (2013) Interest rate - Friendships - Probit regression Crowdlending No Online friendships act as signals of credit quality, 35 Default rate Prosper increase the probability of funding, lower interest 36 rates, and result in lower ex-post default rates— 37 gradation in effects based on roles and identities of friends. 38 39 Zhang and Liu Loan - Crowding - Hazard Model Crowdlending No Well-funded borrowers attract more funding. (2012) Amounts - Fixed Effects Prosper Lenders learn from peer decisions and do not 40 - First differences mimic. 41 42 43 44 45 Journal of Marketing 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 44 of 80

44 1 2 Author Accepted Manuscript 3 Table 2. Summary Statistics for Kickstarter 4 5 Variable Mean SD Min Median Max 6 7 Amount of funding of idea i (in $) on day 409.34 4,764.72 0 0 593,731 8 t 9 Backer affiliation of idea i by day t-1 3.33 10.66 0 0 477 10 (Affilit-1) 11 Cumulative number of backers funding 12 53.85 357.87 0 10 17,018 13 idea i by day t-1 (CumBackersit-1) 14 Number of backers funding idea i on day 6.71 142.99 0 1 17,010 15 t-1 (Backersit-1) 16 17 Cumulative number of updates by creator 8.82 23.64 0 0 524 18 of idea i by day t-1 (PeerCumUpdates Reviewit-1) Version 19 Proportion of funding goal of idea i 20 .49 2.14 0 .13 132.63 achieved by day t-1 (PropGoalit-1) 21 22 Proportion of funding duration of idea i .31 .24 0 .27 .97 23 completed by day t-1 (PropDurationit-1) 24 Closeness centrality of idea i as of day t-1 5.67 x 5.01 x 3.49 x 2.50 x 2.2 x 25 10-9 10-8 10-11 10-10 10-6 26 27 Betweenness centrality of idea i as of day 1258.07 8698.49 0 0 335,213 28 t-1 29 Eigenvector centrality of idea i as of day 2.50 x 30 0.002 0.03 0 -9 1 31 t-1 10 32 Last week (1 if day t is in the last week of 0.08 0.28 0 0 1 33 funding of idea i, 0 otherwise) 34 35 Cumulative number of communal words 36 in updates by creator of idea i by day t-1 20.17 1696.27 0 0 230,232 37 (CommunalUpdatesit-1) 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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45 1 2 Author Accepted Manuscript 3 Table 3. Means of Backer Affiliation and Other Time Varying Covariates at Different 4 Levels of Daily Funding (Kickstarter) 5 6 Variable Amount 7 Amount Amount funded 0 < 8 funded (y ) = funded (y ) > it (y )  it 9 0 it $409.34 10 $409.34 11 12 Number of observations 17,505 11,071 3,863 13 Proportion of all observations 53.96% 34.13% 11.91% 14 15 Amount of funding of idea i (in $) in day t 0 109.29 3,214.89 16 17 Backer affiliation of idea i by day t-1 (Affilit-1) 4.22 2.26 1.79 18 Peer Review Version 19 Cumulative number of backers funding idea i 22.65 44.23 222.89 20 by day t-1(CumBackersit-1) 21 Cumulative number of updates by creator of 6.21 10.54 15.76 22 idea i by day t-1 (CumUpdatesit-1) 23 24 Proportion of funding goal of idea i achieved .26 .53 1.69 25 by day t-1 (PropGoalit-1) 26 Proportion of funding duration of idea i .34 .29 .27 27 completed by day t-1 (PropDuration ) 28 it-1 29 Closeness centrality of idea i as of day t-1 5.68 x 10-9 4.56 x 10-9 9.49 x 10-9 30 Betweenness centrality of idea i as of day t-1 473.99 1178.21 5974.58 31 32 Eigenvector centrality of idea i as of day t-1 33 0.001 0.000 0.012 34 35 Last week (1 if day t is in the last week of 0.10 0.07 0.06 36 funding of idea i, 0 otherwise) 37 Cumulative number of communal words in 38 updates by creator of idea i by day t-1 0.69 1.93 199.38 39 40 (CommunalUpdatesit-1) 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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46 1 Author Accepted Manuscript 2 3 Table 4. Pairwise Correlation Coefficients of all Variables (Kickstarter) 4 5 No. Variable 1 2 3 4 5 6 7 8 9 10 11 6 7 1 Amount of funding of (in $) in t 1 8 2 Backer affiliation i by t-1 -.07 1 9 10 3 Cum. number of backers funding by t-1 .13 .14 1 11 4 Cum. number of updates by creator i by t-1 .13 .15 .18 1 12 5 Amount of funding (in $) inPeer t-1 Review.49 -.11 .17Version.15 1 13 14 6 Proportion of funding goal achieved by t-1 .15 .00 .12 .20 .19 1 15 7 Proportion of funding duration completed by t-1 -.11 .37 .05 .30 -.19 .05 1 16 17 8 Closeness centrality as of t-1 .01 -.07 -.00 -.04 .14 -.01 -.12 1 18 9 Betweenness centrality i as of t-1 .09 .37 .23 .29 .07 .07 .32 -.07 1 19 20 10 Eigenvector centrality as of t-1 .05 .04 .33 .09 .07 .04 .00 .07 .09 1 21 11 Last week (1 if day t is in the last week of funding) -.05 .14 .02 .15 -.05 .05 .61 .03 .01 .00 1 22 23 12 Cum. no. of communal words in updates by t-1 .02 .02 .26 .01 .03 .00 -.00 .01 .00 .09 -.00 24 Notes: We take logarithms of all variables, which are not proportions. For variables that can take zero values, we take the logarithm of the variable added to 25 0.001. All variables pertain to the focal idea. Coefficients with p < 0.05 are in bold. 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 Journal of Marketing 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 47 of 80

47 1 2 Author Accepted Manuscript 3 Table 5. Coefficient Estimates of the Fixed Effects Regression Model of Daily Funding of 4 Ideas on Kickstarter 5 6 7 M5 8 Variable M1 M2 M3 M4 (final 9 model) 10 11 Backer affiliation of idea i by day t-1 -.04*** -.86*** -1.88*** -.80*** -.87*** 12 (Affilit-1) (.01) (.06) (.41) (.06) (.06) 13 Cumulative number of backers funding .15** 1.92*** 4.13*** 1.79*** 2.02*** 14 idea i by day t-1 (CumBackersit-1) (.06) (.15) (.90) (.15) (.16) 15 Cumulative number of updates by 16 -.05** -.06** -.07* -.06** -.05* creator of idea i by day t-1 17 (.02) (.03) (.04) (.03) (.03) 18 (CumUpdatesit-1) Peer Review Version 19 Amount of funding of idea i (in $) on -.03 .01 0.07** 0.01 0.01 20 day t-1 (.02) (.02) (0.03) (0.02) (0.02) 21 22 Proportion of funding goal of idea i -.09 -.23*** -.39** -.22** -.17** 23 achieved by day t-1 (.06) (.07) (.10) (.07) (.07) 24 Proportion of funding duration of idea -.27 1.12 2.84* 1.02 .63 25 i completed by day t-1 (.99) (1.10) (1.65) (1.09) (1.10) 26 27 Closeness centrality of idea i as of day 2.54 2.43 8.46 1.99 2.71 28 t-1 (3.12) (3.54) (5.30) (3.48) (3.54) 29 Betweenness centrality of idea i as of -.02** -.02** -.03* -.02** -.02* 30 day t-1 (0.01) (0.01) (.02) (.01) (0.01) 31 32 Eigenvector centrality of idea i as of -.74 -4.39* -8.95* -4.13* -4.40* 33 day t-1 (.82) (2.33) (4.66) (2.21) (2.67) 34 35 Last week (1 if day t is in the last week .25 .13 .03 .14 .09 36 of funding of idea i, 0 otherwise) (.21) (.24) (.35) (.24) (.25) 37 Cumulative number of communal -.04 -.02 -.01 -.02 -.29* 38 words in updates by creator of idea i 39 (.03) (.03) (.04) (.03) (.15) by day t-1 (CommunalUpdatesit-1) 40 41 Interactions of Backer Affiliations 42 -7.68** 43 Affilit-1 X CommunalUpdatesit-1 44 (3.80) 45 46 Affilit-1 X Number of Facebook -.006*** 47 shares of idea i (.001) 48 49 Fixed effects for each idea i Yes Yes Yes Yes Yes 50 Fixed effects for each day t Yes Yes Yes Yes Yes 51

52 Instrument for Affilit-1 No Yes Constra Other Yes 53 54 *** p < .01, ** p < .05, * p < .10 55 Notes: “Constra” refers to Burt’s measure of constraint (Burt 1992) of the focal idea. “Other” 56 instrument refers to the instrument constructed from Indiegogo data. 57 58 59 60

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48 1 2 Author Accepted Manuscript 3 Table 6. Actionable Outcomes for Managers 4 Recommendations for Idea Descriptions and Updates 5 6 7 Finding Recommendations for Examples from Kickstarter Data 8 Creators 9 10 Focus on the idea’s inherent The Drone Pocket 11 purpose as opposed to a focus Idea Description: “The world's first 12 on community multicopter that's powerful enough to 13 carry a high-quality action camera and 14 15 folds up smaller than a 7 in tablet.” 16 17 Key technology features outlined 18 Peer Review Versionprominently on idea’s home page 19 20 Total Amount Raised: $929,212 21 22 Interaction 23 Pegasus Touch Laser SLA 3D Printer 24 Between Idea page includes recent press articles 25 Affiliation and with links that highlight idea’s features 26 Communal Words 27 in Idea Description Total Amount Raised: $819,535 28 29 Use non-communal words (e.g., The Floyd Leg 30 “you” vs. “we”) in idea “The Floyd Leg gives you the framework 31 description to take ownership of your furniture by 32 allowing you to create a table from any 33 flat surface” (emphasis added) 34 35 36 Total Amount Raised: $256,273 37 Avoid thanking backers too “First off, I want to say thanks for 38 much in idea description as it checking out of project. Every single 39 can make the project appear person that takes the time to look at our 40 needy project means the world to us.” 41 42 Do not describe idea with “As we approach Thanksgiving, I 43 overemphasis on communal continue to be thankful for the patience 44 language (e.g., support, team) and support that the unsung backers have 45 shown with our team.” 46 47 Interaction Do not show too much “Thanks to all of you who pledged for this 48 Between appreciation via updates for campaign. We really appreciate your 49 Affiliation and funding as it is progressing continued support.” 50 Communal Words Minimize communal language “Your first duty as partners with us on this 51 (e.g., partner) in updates project; should you choose to accept...” 52 in Updates 53 54 55 56 57 58 59 60

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1 1 Author Accepted Manuscript 2 3 Figure 1. Illustration of Affiliation in Crowdfunding 4 5 6 No Affiliation Low Affiliation High Affiliation 7 8 9 10 11 12 Peer Review Version 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 The figure represents simplified examples of three levels of affiliation (no affiliation, low affiliation, and high affiliation) for a focal backer. In all 35 three scenarios, backers A, B, and C have already funded a focal idea. Subsequently, a new backer, labeled “focal backer,” also funds the focal 36 idea. This is where the affiliation for the focal backer is formed. In the first scenario, the focal backer has never jointly funded an idea with any of 37 these three backers; hence, there is no affiliation. In the second scenario, the focal backer and backer A have previously jointly funded idea 1; 38 hence, there is low affiliation. In the final scenario, the focal backer has jointly funded with each of the three backers, leading to the highest level 39 40 of affiliation. We propose that affiliation between the focal backer and others will influence the amount that the focal backer puts toward the focal 41 idea. 42 43 44 45 Journal of Marketing 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 50 of 80

1 2 Author Accepted Manuscript 3 Crowdfunding Success: Do Backer Affiliations Help or Hurt? 4 5 Kelly Herd, Girish Mallapragada, Vishal Narayan 6 7 8 List of Web Appendices 9 10 11 Web Appendix A: Heat Map Study 2 12 13 14 Web Appendix B: Vicarious Moral Licensing and Shared Identity Study 4 15 16 Web Appendix C: PeerBacker Interviews, Review Survey, and Version Discussion Forums 6 17 18 Web Appendix D: Summary Statistics for Kickstarter and Indiegogo Ideas 9 19 20 21 Web Appendix E: Figures and Illustrations of Sample Characteristics 12 22 23 Web Appendix F: Coefficient Estimates of the Fixed Effects Regression 24 Model of Daily Funding of Ideas on Indiegogo 19 25 26 Web Appendix G: Analysis with Alternate Instrumental Variables 20 27 28 29 Web Appendix H: Alternate Models and Alternate Measures of Affiliation 23 30 31 Web Appendix I: Controlled Experiments 26 32 33 34 35 36 37 38 These materials have been supplied by the authors to aid in the understanding of their paper. The AMA is 39 40 sharing these materials at the request of the authors. 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 51 of 80

1 2 Author Accepted Manuscript 3 Web Appendix A. Heat Map Study 4 5 6 We conducted the study on Amazon Turk with 100 North American residents (M Age = 38.36 7 years, 37% women). All participants read the following: 8 9 Below, you will see the GoFundMe page for SASS, out of Austin, Texas. There is lots of 10 information available, and we are interested in what information you think is most relevant to 11 your decision whether or not to fund. As you read about this project, please click on any 12 13 information as you are reading it. 14 15 16 17 Peer Review Version 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Participants then clicked on the sections as they read them: 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 52 of 80

1 2 Author Accepted Manuscript 3 Sixty-four participants reported reading at least one piece of information available on the idea 4 5 page, including the following (in order of total across all participants): 6 7 1. Idea description (56) 8 2. User comments (43) 9 3. Creator updates (42) 10 4. Project image (39) 11 5. Total amount raised so far (38) 12 13 6. Information about other funders (32) 14 7. Organizer and beneficiary (28) 15 8. Creator name + date created (26) 16 9. Website FooterPeer (25) Review Version 17 10. Donate Now + Total number of donors, shares, and followers (23) 18 11. Project title (18) 19 20 21 Next, we asked all participants to rank the following backer level variables from 1 = most 22 important to 7 = least important. Averaging the rankings across all participants reveals that co- 23 backing is perceived as more important than other seemingly important information such as 24 backer location and community engagement: 25 26 1. Prior Projects Funded 27 28 2. Projects Created 29 3. Co-backing Relationship (i.e., Whether a backer has funded other projects that you 30 have also funded) 31 4. Backer Location 32 5. Number of Comments Since Joining Community 33 6. Last Login 34 7. Username 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 53 of 80

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2 Author Accepted Manuscript 3 Web Appendix B. Affiliation and Shared Identity Study 4 5 6 To confirm that affiliation does indeed change perceptions of shared identity, an important driver 7 of vicarious moral licensing (Goldstein and Cialdini 2007; Kouchaki 2011), we ran a study with 8 150 Amazon Turk participants (M Age = 36.68 years, 48% women). First, participants were told 9 to imagine that they had $50 and were asked to choose one idea to fund from a set of four real 10 ideas seeking funding on Kickstarter: 11 12 13 1. SoloSocks 14 15 16 17 Peer Review Version 18 19 20 21 22 23 24 25 26 27 2. Meat Hook Sausage Company 28 29 30 31 32 33 34 35 36 37 38 39 40 41 3. diveLIVE 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 54 of 80

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2 Author Accepted Manuscript 3 4. Vision Thru Art 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Peer Review Version 18 Participants then reported their perceived shared identity with these individuals who had chosen 19 to fund the same project (i.e., high affiliation) or not (i.e., no affiliation). First, on 7-point Likert 20 scales (strongly disagree/strongly agree), they reported how much they felt a connection, would 21 have things in common, would have similar interests, are more similar than different, and feel 22 more connected to these individuals than to others in the Kickstarter community (M = 4.04, SD = 23 1.33; α = .93). Next, participants completed one well-established measure of closeness (i.e., 24 25 shared identity) in which participants are shown concentric circles that overlap to varying 26 degrees and asked to mark the picture that describes their degree of shared identity with these 27 individuals (M = 3.31, SD = 1.57; Aron et al. 1992; Newman and Brucks 2018). As expected, 28 those in the high affiliation condition felt a stronger shared connection (Mhigh = 4.54, SD = 1.10 29 vs. Mno = 3.56, SD = 1.35; F(1, 148) = 23.55 p < .01) and greater overlap than those in the no 30 affiliation condition (Mhigh = 3.60, SD = 1.50 vs. Mno = 3.03, SD = 1.60; F(1, 148) = 5.04, p < 31 .03). 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 55 of 80

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2 Author Accepted Manuscript 3 Web Appendix C. Backer Interviews, Survey, and Discussion Forums 4 5 6 Interviews with Backers 7 8 We conducted a series of semi-structured interviews with six backers (M Age = 41.2 years, 33.3% 9 women). Individuals were recruited for interviews after they responded to a brief request posted 10 on LinkedIn and Facebook by the authors. The initial request only stated that we were interested 11 in talking to people who had funded ideas on crowdfunding platforms. Participants varied across 12 13 profession, including marketing professionals, academics, and artists; participants also included 14 both frequent and infrequent backers, ranging from 1 to 15 total ideas funded. All of the 15 interviews took place over Zoom and lasted between 20 and 30 minutes. Five of the six 16 participants agreed toPeer be recorded Review (transcripts are available Version upon request). 17 18 The major themes that emerged across the six interviews could be summarized as follows: All 19 the individuals felt that a motivation to be part of a community and contribute to something 20 21 bigger than themselves was instrumental to their participation. Some also opined that 22 crowdfunding platforms allow others to find interesting projects and figure out how to be early 23 adopters of technology. Most of them indicated that they observed others’ behaviors on the 24 platforms, particularly participation on the idea’s page and Q&A with the creators. While we 25 found they had mixed opinions about whether others would explicitly influence them, they all 26 indicated that they thought others on the platforms would be affected by such views. There were 27 28 mixed views about guilt concerning not funding projects; and a few stated that they shared ideas 29 through their social media. It also seemed that they mostly looked to what is happening on the 30 idea’s page to make funding decisions. 31 32 Overall, based on the interviews, we found evidence that people look to backer behavior as they 33 participate on the platform and gather most information from Q&A, idea description, creator 34 engagement, and backer engagements. Below are some quotes that reflect each of these topics. 35 36 37 • Prosocial motivation to participate: 38 o “I feel moral pressure to fund.” 39 o “It is a combination of I feel that helping and being part of a community makes 40 sense.” 41 42 43 • How they find projects: 44 o “I found the early projects I funded when I heard about them in media.” 45 o “I am interested in technology and seek out innovative ideas.” 46 47 • Observing other backers’ behaviors and licensing: 48 “I look at other funders only to further discover related projects. It’s an interesting 49 o 50 way to discover – because some people are more involved than you are in this 51 kind of the crowdfunding world – and so it’s interesting to follow that rabbit trail 52 and see ‘oh this person supported this and look at what else they fund,’ there are 53 these other smaller microcosms of whatever that interest is.” 54 o “I notice who else is funding and see them on the page, sometimes engaging with 55 the creator.” 56 57 58 59 60 Journal of Marketing Page 56 of 80

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2 Author Accepted Manuscript 3 “If I continue, then I would probably look for, you know, where else is this guy 4 o 5 funding, you know? .... I would love to see because if I feel like he is funding or 6 he or she is finding interesting projects, then I would go look to see what else they 7 are funded. After all, I’m sure there’s something in there which I’ve missed.” 8 o “You are dealing with finite resources in terms of what you are willing to spend. 9 If you support one thing, I don’t know, for me, if I see someone supporting 10 something else, I think, well yeah, they supported that. I’m sure I could find a 11 bunch of other people that support a bunch of other things. I just gave X amount 12 13 of dollars, whatever amount I have, and I’m not going to be giving any more than 14 that right now.” 15 16 Survey of Backers Peer Review Version 17 18 We surveyed 100 backers (M Age = 37.29 years, 30% women) who were pre-screened on prior 19 crowdfunding behavior through Prolific, an online participant recruitment site. 20 21 22 First, we asked them to evaluate information found on a crowdfunding website (e.g., idea 23 description, user comments). These participants then assessed each piece of information on a 7- 24 point Likert scale (not important/very important). We found that while not as important as some 25 other information, e.g., creator updates, “the presence of other co-backers (i.e., others who have 26 funded ideas that you have previously funded)” is perceived as important, M = 4.14, which is 27 28 significantly above the midpoint of the scale (t(99) = 4.05, p = .000). 29 30 Next, to further demonstrate that prosocial motives are prominent in crowdfunding contexts, we 31 asked these backers about their motivation to donate. As expected, the desire to support a cause 32 (64%), help others (48%), bring valuable products and services to the marketplace (64%), and be 33 part of a community (25%) were prominent. Participants also reported the desire to gain financial 34 returns (41%), collect rewards (41%), gain recognition (7%), and avoid boredom (4%). This 35 36 suggests that while not all backers are motivated by purely prosocial goals, these goals are 37 common. These findings are in line with findings from other research (e.g., Simpson et al. 2021). 38 39 To further validate our proposed mechanism, we explicitly asked participants how much they 40 disagree/agree with three statements about affiliation and vicarious moral licensing. 41 42 • 43 “Backers I overlap with on other ideas (i.e., those who have previously funded an idea 44 that I fund) can inform my funding behavior” (1 = strongly disagree, 7 = strongly agree), 45 M = 4.27. This is significantly above the midpoint (t(99) = 5.12, p = .000). 46 • “When other people with whom I have co-backed fund a new idea, I feel less of a need 47 also to fund” (1 = strongly disagree, 7 = strongly agree), M = 3.75. This is marginally 48 above the midpoint (t(99) = 1.96, p = .053). 49 50 • “When other people with whom I have co-backed fund a new idea, I sometimes feel 51 justified in not funding” (1 = strongly disagree, 7 = strongly agree), M = 3.84. This is 52 significantly above the midpoint (t(99) = 2.50, p = .014) 53 54 55 56 57 58 59 60 Journal of Marketing Page 57 of 80

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2 Author Accepted Manuscript 3 Taken together, we find that backers are often motivated by prosocial motives, and when 4 5 affiliated others fund an idea, they felt less of a need to fund, further validating the proposed 6 vicarious moral licensing mechanism. 7 8 Backer Discussion Forum Posts 9 10 11 We gathered 572 discussion posts from kickstarterforums.org on the following three relevant 12 threads: 1) “How many ideas have you backed and why do you back them?” (n = 317), 2) 13 “What’s the most interesting idea you’ve backed?” (n = 247), and 3) “What’s the worst idea you 14 have backed? And why?” (n = 8). Next, we used text analysis (bag of words approach) to code 15 conversations among backers. 16 17 Peer Review Version 18 We coded the posts on three dimensions established in the LIWC (2015) dictionary: communal, 19 innovation, and exchange. Our goal was to see the relative emphasis of comments on communal 20 compared to innovation, the dominant focus of crowdfunding platforms, and to exchange, 21 another commonly discussed motive in crowdfunding. This dichotomy also maps onto our 22 Experiment 3, where we look at the heterogeneity in the effect of affiliation depending on the 23 idea’s description. We cross-validated each category’s words with those available in LIWC 24 25 (2015) categories (Pennebaker et al. 2015). Consistent with our prediction that prosocial goals 26 drive behavior in crowdfunding platforms, we see that participants were much more likely to use 27 communal words (count = 582) than to discuss innovation (count = 230) or exchange-based 28 motives (count = 13). 29 30 Results indicated that in addition to focusing on creator information, they do notice the behavior 31 of backers and, as such, are motivated by a desire to feel like a part of a broader community: 32 33 34 • “Pebble watch was what got me into Kickstarter. Since then, I have backed 10 projects. I 35 think the aggregate feeling I get from a project on 3 fronts: creativity, passion, and utility, 36 determine if there is enough impulse to back a project. Some [projects] I simply have no 37 connection within my realm, but the genuine presentation from the creator and the feeling 38 that it will, on the net, bring goodness to the world will nudge my mouse towards the 39 40 pledge button.” 41 • “I’ve backed 6 projects…I am astounded by some of the big backers out there.” 42 • “I’ve backed 12 projects so far. To be honest, I first started backing because I wanted 43 others to back my new project. But now I’m hooked and wish I could afford to back a ton 44 more. I love backing projects in general because the whole idea is just awesome. It really 45 shows how powerful the internet can be when it’s used for something positive and helps 46 47 foster the creative spirit.” 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 58 of 80

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2 Author Accepted Manuscript 3 Web Appendix D. Summary Statistics For Kickstarter and Indiegogo Ideas 4 5 Table W1. Category Level Statistics for Kickstarter Ideas (N = 2021) 6 7 Average Amount Average Facebook Number of Category 8 Goal Shares Projects 9 10 Film & Video 59,211 169 1258 11 Music 11,918 140 1015 12 Publishing 12,151 91 880 13 Games 34,280 208 594 14 15 Art 12,913 92 576 16 DesignPeer Review22,516 Version144 477 17 Fashion 12,735 276 409 18 Food 16,969 133 333 19 20 Technology 44,990 154 296 21 Comics 6,595 122 205 22 Theater 9,210 83 179 23 Photography 7,968 97 177 24 25 Dance 6,694 83 79 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 59 of 80

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2 Author Accepted Manuscript 3 Table W2. Category Level Statistics for Indiegogo Ideas (N = 2012) 4 5 Average Average Number of 6 Category Amount Facebook Ideas 7 Goal Shares 8 9 Film 41,996 130 495 10 Music 8,856 124 268 11 Theater 9,350 93 198 12 Art 16,689 97 147 13 14 Technology 255,190 488 127 15 Community 74,407 187 114 16 SmallPeer Business Review35,027 Version102 94 17 Education 134,852 112 71 18 19 Video / Web 23,848 126 65 20 Writing 10,141 88 60 21 Design 507,389 210 54 22 Food 33,601 201 54 23 24 Dance 5,822 89 43 25 Health 132,370 67 39 26 Gaming 23,494 74 32 27 Photography 15,230 145 29 28 29 Fashion 16,050 98 25 30 Environment 115,826 126 23 31 Politics 2,148,968 49 17 32 Sports 378,643 286 15 33 34 Comic 6,156 86 14 35 Animals 49,007 53 12 36 Transmedia 28,356 82 9 37 Religion 46,804 19 6 38 39 Faith 500 0 1 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 60 of 80

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2 Author Accepted Manuscript 3 Table W3. Summary Statistics for Indiegogo Ideas 4 5 Variable Mean SD Min Median Max 6 447.82 2,371.82 0 125 122,507 7 Amount of funding of idea i (in $) on 8 day t 9 Backer affiliation of idea i by day t-1 .37 1.79 0 0 90 10 (Affilit-1) 11 Cumulative number of backers 249.08 506.94 0 57 16,883 12 funding idea i by day t- 13 1(CumBackersit-1) 14 15 Cumulative number of updates by 410.91 1726.96 0 97 13,949 16 creator of idea i byPeer day t-1 Review Version 17 (CumUpdatesit-1) 18 Proportion of funding goal of idea i .52 1.94 0 .26 12.73 19 achieved by day t-1 (PropGoalit-1) 20 Proportion of funding duration of idea .42 .21 .01 .42 1 21 i completed by day t-1 22 23 (PropDurationit-1) 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 61 of 80

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2 Author Accepted Manuscript 3 Web Appendix E. Figures and Illustrations Describing Data Sample 4 5 It is theoretically possible that affiliation is restricted to a small subset of ideas. We provide 6 evidence that affiliation is commonly prevalent in our data. We first show (in Figure A1) that 7 8 affiliation takes zero values for less than 60% of idea-day level observations and that the 9 proportion of observations with very high values of affiliation (greater than 10) is only about 8%. 10 Next, we show (in Figure A2) that at an idea level, the mean value of affiliation (across days) 11 exceeds zero for over 1,500 out of 2,201 ideas and that the mean value of affiliation exceeds 10 12 for just 183 ideas. Figure A3 shows that the mean level of affiliation varies substantially across 13 categories (between 2.3 and 4.6), but that it does not take extremely high or extremely low 14 values for any one category. 15 16 To analyze temporalPeer trends, we plotReview the mean value (acrossVersion ideas) of affiliation across days of 17 the funding window (Figure A4). We find an increasing trend of affiliation over the funding 18 19 window and note that this increase is not monotonic. For some days, the mean level of affiliation 20 is lower than that in the previous day. Our results are unaffected by this trend since we 21 incorporate day-specific fixed effects. We notice a similar trend when we plot the mean of the 22 ratio of affiliation to the number of backers for each day of the funding window (Figure A5). All 23 figures appear at the end of this document. 24 25 In addition to statistics about affiliation and to provide visual intuition, we graphed the network 26 structure on day 1, day 18, and day 37 using a bipartite network representation – i.e., both 27 projects (green nodes) and backers (pink nodes) are visible. Please see Figures A6 and A7. As a 28 thought experiment, if there were no affiliation, each project would gather some stand-alone 29 30 backers and end up as an island. On the contrary, the graphs show that over time, affiliations 31 become the basis for the community and crowd-behavior to play out. Although some projects 32 remain as islands, i.e., their backers have no affiliations with others, these decline over time as 33 affiliations continue to increase. On day 1, 4 out of 34 projects (i.e., about 11%) are part of the 34 continuously connected giant component of the network, i.e., these projects are not islands. By 35 day 18, this number is 486 out of 1033 projects (47%). By the last day of our data collection, 36 37 most projects – 1921 out of 2587 (74%) are not islands and are part of one continuously 38 connected component due to affiliations. 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 62 of 80

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2 Author Accepted Manuscript 3 Figure W1. Distribution of Affiliation Measure Across Idea-Day Level Observations (% of 4 5 observations for various levels of affiliation) 6 7 8 58.7 9 10 11 12 13 14 15 16 17 Peer Review Version 18 12.1 11.9 19 5.8 7.6 20 3.2 21 0.8 22 23 0 1-2 3-4 5-6 7-8 9-10 Over 10 24 25 Note: to be read as “affiliation for 12.1% of observations is 1 or 2; affiliation for 7.6% of 26 27 observations is 10 or more” 28 29 30 Figure W2. Distribution of Mean Daily Affiliation Across Ideas (number of ideas for 31 32 various levels of mean affiliation) 33 566 34 536 35 518 36 37 38 39 40 41 42 43 183 44 45 122 46 62 47 35 48 49 50 0 0.01-2 2.01-4 4.01-6 6.01-8 8.01-10 Over 10 51 52 Note: to be read as “across all days that ideas sought funding, the mean daily affiliation for 566 53 ideas is between 2.01 and 4” 54 55 56 57 58 59 60 Journal of Marketing Page 63 of 80

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2 Author Accepted Manuscript 3 Figure W3. Mean Daily Affiliation for Each Category of Ideas 4 5 6 Mean Affiliation Across Categories 7 4.59 8 4.14 9 3.79 3.75 10 3.39 3.11 3.11 3.01 2.97 11 2.94 2.75 12 2.44 2.26 13 14 15 16 17 Peer Review Version 18 19 20 21 22 23 24 25 26 27 28 Figure W4. Mean Affiliation (Across Ideas) For Each Day of Funding Window 29 30 Mean Affiliation Over Time (in Days) 31 32 14 33 34 12 35 36 10 37 38 8 39 6 40 41 4 42 43 2 44 45 0 46 1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 64 of 80

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2 Author Accepted Manuscript 3 Figure W5. Mean of Ratio of Affiliation to Number of Backers, For Each Day of Funding 4 5 Window 6 7 0.35 8 9 0.3 10 11 0.25 12 13 0.2 14 15 0.15 16 17 Peer Review Version 18 0.1 19 20 0.05 21 22 0 23 1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 65 of 80

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2 Author Accepted Manuscript 3 Figure W6. Bipartite Networks Showing Projects and Backers for Day 1 and Day 18 in the 4 5 Data Collection Window (green nodes are projects and red nodes are backers) 6 7 Panel A: Day 1 8 9 10 11 12 13 14 15 16 17 Peer Review Version 18 19 20 21 Panel B: Day 18 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 66 of 80

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2 Author Accepted Manuscript 3 Figure W7. Bipartite Network for Day 37 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Peer Review Version 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 Notes: Pictorial representation of the network in the Kickstarter data sample at various visual 47 granularity levels (see Figure A8). There are a total of 210,798 unique relationships in the 48 sample among 1921 ideas and 170,989 backers. Pink nodes (visible in Panel A and Panel B in 49 Figure A8) are backers, and green nodes are ideas. Small clusters show how a single idea attracts 50 many backers. Higher resolution images are available from the authors. Images were generated 51 52 using Gephi 0.6. 53 54 55 56 57 58 59 60 Journal of Marketing Page 67 of 80

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2 Author Accepted Manuscript 3 Figure W8. Bipartite Network on Day 37 at Different Levels of Magnification 4 5 6 Panel A: Zoom Level 1 7 8 9 10 11 12 13 14 15 16 17 Peer Review Version 18 19 20 21 22 23 24 25 26 27 28 29 Panel B: Zoom Level 2 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 68 of 80

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2 Author Accepted Manuscript 3 Web Appendix F. Analysis of Indiegogo Data 4 5 Table W4. Coefficient Estimates of the Fixed Effects Regression Model of Daily Funding of 6 Ideas on Indiegogo 7 8 Variable M1 M2 9 Backer affiliation of idea i by day t-1 -.29*** -.27*** 10 (Affilit-1) (.09) (.10) 11 Cumulative number of backers 12 .11 .17*** funding idea I by day t-1 13 (.06) (.06) 14 (CumBackersit-1) 15 Cumulative number of updates by 1.19*** 1.21*** 16 creator of idea i by day t-1 Peer Review Version(.38) (.40) 17 (CumUpdatesit-1) 18 Amount of funding of idea i (in $) in .07*** .07 19 20 day t-1 (.03) (.04) 21 Proportion of funding goal of idea i .36*** .37*** 22 achieved by day t-1 (.10) (.10) 23 Proportion of funding duration of idea .08 .10*** 24 i completed by day t-1 (.05) (.05) 25 Fixed effects for each idea i Yes Yes 26 Fixed effects for each day t Yes Yes 27 28 Instrument No Yes 29 *** p < .01, ** p < .05, * p < .10 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 69 of 80

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2 Author Accepted Manuscript 3 Web Appendix G. Analysis With Alternate Instrumental Variables 4 5 6 1. Latent Instrumental Variables 7 8 To demonstrate robustness to different instruments, we first employ latent instruments. This 9 method yields consistent estimates, has reasonable power over a wide range of regressor-error 10 correlations and is widely used in marketing (Ebbes et al. 2005, Rutz et al. 2012, Zhang and Godes 11 12 2018). We decompose each endogenous regressor into a systematic part uncorrelated with the 13 funding equation’s error term and a part potentially correlated with the error term. Thus, we 14 specify, 15 16 log (Affil + 0.001) = z + (W1) 17 Peer Review Version 18 where z it−1 is an unobservedπ� it(or−1 latent)ϑit−1 and discrete instrument of dimensionality and is 19 independent of the error term by construction. is a vector of m coefficients. To relax the 20 it−1 assumption� that backer affiliation is independent of the error term associated with the funding 21 π 22 model, we allow for the error term associated with equation W1 correlated with the error term of 23 the funding model (equation 1) as follows: 24 25 [ ]~MVN(0, ) (W2) 26 27 ε itis anϑ itunrestricted−1 covarianceΣ matrix. We specify the instrumental variablez , to follow a 28 multinomial distribution with parameters (1, λ) where λ = (λ , λ ,… λ ) and λ = 1. λ is 29 it−1 Σthe probability that the rth latent instrument for the endogenous regressor is 1,� i.e.,m the rth element 30 1 2 m r=1 r r of the vector z is one (and all other elements are zero). Identification requires∑ the number of 31 32 categories m to exceed one. Ebbes et al. (2005) discuss the properties of the LIV estimator, prove it−1 33 that it is identifiable� through the likelihood and show that this method for mitigating endogeneity 34 bias works over a wide range of regressor-error correlations and across several distributions of 35 36 instruments. Estimates of close to zero or one are undesirable since they might indicate 37 insufficient variation in the data to separate the endogenous regressor into discrete categories. r 38 λ 39 The LIV approach assumes that the measure of affiliation (the endogenous regressor) can be 40 decomposed into two parts: an endogenous part (which is discrete) and an exogenous part. 41 Although this assumption is empirically untestable, we note that our endogenous regressor is 42 43 categorical. So, separating it into two parts, one of which is discrete, has conceptual appeal. The 44 exogenous part could capture those co-backing relationships which trigger licensing, i.e., 45 encourage backers to act in ways that differ from those of their affiliated backers. The 46 endogenous part could capture the role of homophily or other unobserved characteristics that are 47 48 common between affiliated backers – and which could potentially be correlated with the error 49 term of the funding equation. 50 51 We use a Bayesian approach to estimate the model. We specify diffuse and uninformative prior 52 distributions for model parameters and derive their posterior conditional distributions. Given the 53 set of conditional distributions and priors, we draw recursively from the posterior distribution of 54 the model parameters. We use data augmentation to draw the vector of instruments z , 55 56 it−1 57 � 58 59 60 Journal of Marketing Page 70 of 80

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2 Author Accepted Manuscript 3 obviating the need to integrate it out. As the LIV estimator is robust to the number of categories 4 5 (Ebbes et al. 2005), we choose the latent instrument to have two categories (i.e., m = 2). 6 7 Our estimate of the effect of affiliation using the LIV approach (posterior mean = -0.71, posterior 8 SE = 0.08), falls between the OLS estimate ( = -0.04, p < .01), and the estimate using the 9 observed instrument constructed from other categories ( = -0.86, p < .01). As Papies et al. 10 (2017) suggest, it is reassuring that all estimates𝛽𝛽 are directionally similar, and that the LIV 11 estimate is not very different from other estimates. 𝛽𝛽 12 13 As a robustness check, we estimate the LIV based model for different numbers of categories of 14 15 instruments. For m = 2, 3 and 4, the LIV estimates are -0.74 (p < .01), -0.77 (p < .01) and -0.86 16 (p < .01) respectively. Similar estimates are reassuring. Very different estimates for different 17 values of m could indicatePeer an ill -Reviewdefined instrument. Version 18 19 The LIV approach relies on the non-normality of the endogenous regressor. We test for non- 20 normality by conducting the Shapiro-Wilk test and the Shapiro-Francia test. The null hypothesis 21 ( + 0.001) 22 is that is normally distributed. The Shapiro-Wilk test statistic and the Shapiro-Francia test statistic are 0.917 (Prob > z = 0.000) and 0.919 (Prob > z = 0.000) 23 𝑖𝑖𝑖𝑖−1 24 respectively.𝑙𝑙𝑙𝑙𝑙𝑙 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 Both𝐴𝐴 tests easily reject the null hypothesis. 25 26 Estimates of close to zero or one are undesirable since they might indicate insufficient 27 variation in the data to separate the endogenous regressor into discrete categories. Our posterior r 28 estimates of λ and are 0.27 (p < .01) and 0.73 (p < .019) respectively, suggesting sufficient 29 variation to separate the endogenous regressor. 30 1 2 31 λ λ 32 2. Two Observed Instruments 33 34 Next, we use an observed instrument based on affiliation from another platform. For the model 35 of Kickstarter data, we used the mean of affiliation (across ideas) in our Indiegogo data on the t- 36 th 37 1 day from the commencement of funding of the ideas on Indiegogo as an instrument. This is a 38 valid instrument if correlated with the endogenous variable but not with the error term in 39 equation 1. The correlation between the endogenous variable (affiliation on Kickstarter) and the 40 instrument (from Indiegogo) is 0.21, consistent with the view that affiliations are fundamental to 41 social structure and evolve similarly across crowdfunding platforms. Indeed, network research 42 has shown the commonality of such growth in various settings (e.g., Barabási and Albert 1999). 43 The instrument from Indiegogo, in addition to being from a different platform, is less likely to 44 45 affect the funding success of ideas in Kickstarter data as the instrument is from a future time- 46 period and does not exist on day t of the Kickstarter data. It also varies over time, much like the 47 endogenous variable. 48 49 Finally, we used an observed instrument based on co-backing behavior that varies both across 50 time and over ideas. For this, we calculated Burt’s measure of constraint (equation 2.4, p. 55 of 51 Burt (1992)) of the idea i in time t as an instrument for affiliation. Constraint indicates structural 52 53 holes in networks, a measure of unexploited opportunities to connect other nodes, and has been 54 used in network research (Mallapragada et al. 2012). The constraint of a focal idea indicates the 55 extent to which it acts as the only connecting point for backers to be affiliated with each other. If 56 an idea’s constraint is low, it means that backers had other opportunities to co-back through other 57 58 59 60 Journal of Marketing Page 71 of 80

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2 Author Accepted Manuscript 3 ideas. Thus, conceptually, constraint is negatively related to affiliation. We expect greater 4 5 affiliation among ideas with low constraint. The correlation between log of affiliation and log of 6 constraint in our data is -0.39. Further, constraint satisfies the exclusion restriction. The focal 7 idea’s constraint measure in t-1 cannot affect funding in t because the constraint is only reflective 8 of backers at t-1 and not backers who decide to fund on a focal day. We control for funding in 9 day t-1 and employ fixed effects for each idea and day. So, the error term in equation 1 captures 10 unobservables specific to the focal idea and day t. Constraint is also not an obvious metric, and 11 we do not expect backers to consider it when making funding decisions. Thus, there is a limited 12 13 conceptual argument to link constraint to funding directly. Thus, constraint satisfies both 14 relevancy and exclusion criteria as an instrument. We find that results are consistent and robust 15 across the various analyses. 16 Peer Review Version 17 References 18 19 Barabási, Albert-László, and Réka Albert (1999), "Emergence of scaling in random 20 21 networks," Science, 286 (5439), 509-512. 22 23 Burt, Ronald S. (1992), "Structural Holes: The Social Structure of Competition". Boston: MA: 24 Harvard University Press. 25 26 Mallapragada, Girish, Rajdeep Grewal, and Gary Lilien (2012), "User-Generated Open Source 27 28 Products: Founder's Social Capital and Time to Product Release," Marketing Science, 31 (3), 29 474-92. 30 31 Ebbes, Peter, Michel Wedel, Ulf Böckenholt, and Ton Steerneman (2005), "Solving and Testing 32 for Regressor-Error (in) Dependence When no Instrumental Variables are Available: With New 33 Evidence for the Effect of Education on Income," Quantitative Marketing and Economics, 3 (4), 34 365-92. 35 36 37 Rutz, Oliver J., Randolph E. Bucklin, and Garrett P. Sonnier (2012), "A Latent Instrumental 38 Variables Approach to Modeling Keyword Conversion in Paid Search Advertising," Journal of 39 Marketing Research, 49 (3), 306-19. 40 41 Zhang, Yuchi and David Godes (2018), "Learning From Online Social Ties," Marketing Science, 42 37 (3), 425-44. 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 72 of 80

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2 Author Accepted Manuscript 3 Web Appendix H. Alternate Models and Alternate Measures of Affiliation 4 5 6 1. Probit and Logit Models of Funding Success 7 8 Creators can avail of funds raised on Kickstarter only if the funding goal is reached. To assess 9 the effect of affiliation on funding success, we estimate a Probit model, where the outcome is the 10 funding success of idea i (1 if idea i received funding equal to its goal or more, and 0 otherwise). 11 The key independent variable is the cumulative number of co-backings between all backers who 12 funded the focal idea ( = ). Control variables include the cumulative number 13 of backers during the duration of funding,𝑇𝑇𝑖𝑖 the cumulative number of updates, the cumulative 14 𝑖𝑖 ∑𝑡𝑡=1 𝑖𝑖𝑖𝑖 15 number of positive, negative,𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 and communal𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 words in updates, the goal amount, the funding 16 duration, the number of Facebook shares of the idea, means (over the funding window) of the 17 three network measures,Peer and the Reviewinteraction terms from Version Equation 1. Again, we find a negative 18 effect of affiliation (see table on the next page). The effect is robust to replacing the cumulative 19 / 20 measure of affiliation with the mean level of affiliation across all days ( ). A 𝑇𝑇𝑖𝑖 21 negative effect of affiliation across datasets of different levels of aggregation demonstrates the 𝑡𝑡=1 𝑖𝑖𝑖𝑖 𝑖𝑖 22 robustness of our results1. Replacing the Probit with a Logit model yields∑ similar𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 results𝐴𝐴 𝑇𝑇 2. 23 24 2. Tobit Model of Daily Funding 25 26 Since daily funding cannot take negative values, we specify a logarithmic transformation in 27 equation 1. Another econometrically valid technique to deal with non-negativity in the funding 28 29 data is to estimate a censored regression (or Tobit) model of daily funding. 30 On days that the distribution of backer preferences for the idea exceeds a threshold, the idea 31 32 generates positive funding. In this model, yit is determined by a normally distributed latent * * * 33 variable yit such that = yy itit if yit > 0 ; else yit = 0 . 34 35 We estimate this model using the same covariates as equation 1, but without taking any 36 logarithms. Since unconditional fixed effects are known to bias estimates of this model, we 37 2 38 replace them with random effects, i.e., αi ~N(α,σ ). We again find a negative effect of affiliation; 39 estimates appear later in this Appendix. 40 41 3. Effect of Alternate Measures of Affiliation 42 43 We assess the robustness of our findings to two alternative measures of affiliation. First, instead 44 of using Affilit-1, which is based solely on the number of co-backings between backers who fund 45 on day t-1, and backers who fund before that day, we constructed a cumulative measure 46 47 ( ), the sum (across days) of all co-backings between backers who fund on a specific𝑡𝑡−1 day and backers who fund before that day. Second, we define Affilit-1 = 1 if there is at 48 𝑡𝑡=1 𝑖𝑖𝑖𝑖−1 49 least∑ one𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 co𝐴𝐴-backing between a backer who funded idea i on day t-1 and a backer who funded 50 51 52 1 Indiegogo allows creators to collect funds even if the funding goal is not met. Because of this, we restricted the 53 analysis of “funding success” to the Kickstarter dataset. 54 2 We also estimated a negative binomial model of the number of backers of idea i on day t, and found a negative 55 effect of on the number of backers thus ruling out the explanation that prior affiliation leads to funding by 56 more backers, but that these backers fund less. Details of this analysis are available on request. 57 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖−1 58 59 60 Journal of Marketing Page 73 of 80

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2 Author Accepted Manuscript 3 idea i before that day; 0 otherwise. This is a binary measure that is less time consuming and 4 5 cognitively less effortful for potential backers to compute than our original measure, and 6 therefore theoretically appealing. We estimate the IV regression model described earlier with 7 each of these measures and find negative coefficients for all of them. The coefficient of the 8 cumulative measure of affiliation is -0.43 (p < .01), and that of the binary measure is -14.04 (p < 9 10 .01). 11 12 4. Disaggregate model at the backer-idea-day level 13 14 We constructed a dataset at the backer-idea-day level and estimated a model of backer funding. 15 We found a negative effect of backer affiliation on backer funding, after controlling for both 16 observed and unobservedPeer backer Reviewcharacteristics. First, Version we randomly sampled 95 ideas from the 17 Kickstarter dataset of over 2,000 ideas. 11,189 backers have funded at least one of these ideas in 18 our dataset. So we constructed a dataset at the backer-idea-day level, based on these 95 ideas and 19 20 11,189 backers, such that the binary dependent variable (fundbit) is 1 if backer b funds idea i on 21 day t, and 0 otherwise. This leads to a dataset of 7,216,905 observations, with fundbit taking zero 22 values for 99.8% of all observations. Next, we estimated the following linear probability model, 23 which utilizes the same covariates as the proposed model. yit-1 is the monetary funding received 24 by an idea i on day t. 25 26 α α α β β β β β 27 fundbit = i + t + b + 1Affilit-1 + 2CumBackersit-1 + 3CumUpdatesit-1 + 4yit-1 + 5PropGoalit-1 28 + β6PropDurationit-1 + β7LastWeekit-1 + β8Networkit-1 + εit 29 30 In addition to the time varying backer level covariate (CumBackersit-1), this model controls for 31 backer specific unobservables by incorporating backer fixed effects (αb). The error term is 32 assumed normally distributed. A non-linear model (such as logit) proved infeasible to estimate 33 34 presumably due to the large number of fixed effects, and the sparseness of the dependent 35 variable. Parameter estimates are consistent with those obtained from the models specified in the -5 36 paper. In particular, the coefficient of affiliation is negative and significant ( = -6.85 X 10 , p < 37 .01), and the coefficient of the cumulative number of backers (potentially capturing herding 38 effects) is positive and significant ( = 0.000234, p < .01). 𝛽𝛽 39 40 𝛽𝛽 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 74 of 80

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2 Author Accepted Manuscript 3 Table W5. Coefficient Estimates of the Probit and Logit Models of Funding Success 4 5 6 Variable Probit Model Probit Model Logit Model with 7 with cumulative with mean daily cumulative 8 backer affiliation backer affiliation backer affiliation 9 ( ) ( / ( ) 10 𝑻𝑻𝒊𝒊 𝑻𝑻𝒊𝒊 ) 𝑻𝑻𝒊𝒊 11 𝒕𝒕=𝟏𝟏 𝒊𝒊𝒊𝒊 𝒕𝒕=𝟏𝟏 𝒊𝒊𝒊𝒊 𝒕𝒕=𝟏𝟏 𝒊𝒊𝒊𝒊 Idea level measure of backer ∑ -.029***𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨 ∑ -.279***𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨 ∑ -.064***𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨 12 𝒊𝒊 13 affiliation (.008) (.023)𝑻𝑻 (.016) 14 Cumulative number of backers .005*** .004*** .012*** 15 (.0005) (.001) (.001) 16 Cumulative number of updates by .005*** .007*** .009*** 17 Peer Review Version 18 creator (.001) (.001) (.002) 19 Goal Amount (in $) (103) -.026*** -.039*** -.060*** 20 (.02) (.03) (.005) 21 22 Duration of funding (in days) -.011*** -.013*** -.02*** 23 (.004) (.003) (.01) 24 Closeness centrality of idea i 0.97 1.17 1.10 25 (mean over days) (1.97) (2.31) (3.43) 26 27 Betweenness centrality of idea i -.005 -.006 -.018 28 (mean over days) (.006) (.006) (.011) 29 Eigenvector centrality of idea i .88 1.20 1.38 30 (mean over days) (2.22) (2.13) (3.68) 31 32 Number of communal words in .58*** .59*** 1.21*** 33 all updates by creator of idea i (.11) (.12) (.23) 34 (CommunalUpdatesi) 35 Number of Facebook shares of .09*** .09*** .14*** 36 idea i (FBSharesi) (.01) (.02) (.01) 37 38 Affili X CommunalUpdatesi -.003*** -.003*** -.007*** 39 (.001) (.001) (.002) 40 Affili X FBSharesi -.0004*** -.0003*** -.007*** 41 (.0001) (.0001) (.0002) 42 *** p < .01, ** p < .05, * p < .10; All models include category specific fixed effects. 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Marketing Page 75 of 80

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2 Author Accepted Manuscript 3 Web Appendix I. Controlled Experiments 4 5 6 In all of our studies, we included an open-ended question asking participants about their 7 funding decision. Participants were removed if they noted that they had a personal connection 8 to the idea’s specific product or cause. In experiment 1, four participants were removed who 9 indicated they had a child affected by autism, the focus of one of the two ideas and were 10 inclined toward funding, but would opt to put the money toward helping their child. No 11 12 participants mentioned connection to the other idea’s cause. In experiments 2 and 3, no 13 personal connections were disclosed – perhaps unsurprising, given the ideas included in these 14 studies. 15 16 Experiment 1: Additional Details 17 18 Peer Review Version 19 First, participants saw the following web page for the first idea seeking funding and asked if 20 they would like to fund: 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 All participants who opted to fund then read: 43 44 As a reminder, you previously funded Vision Through the Arts, which already has 45 received pledges for over $1,000 from 206 backers including the following: 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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2 Author Accepted Manuscript 3 Participants were then exposed to the affiliation manipulation. Those in the high affiliation 4 5 condition saw the following web page, which includes high overlap in backers who funded 6 the first idea: 7 8 9 10 11 12 13 14 15 16 17 18 Peer Review Version 19 20 21 22 23 24 25 26 27 28 29 Those in the control affiliation condition saw the following web page, which includes no 30 31 overlap in backers who funded the first idea: 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 This experiment included a manipulation check to confirm the effectiveness of the affiliation 55 manipulation. Participants across both conditions were asked, “Of the people funding Vision 56 Thru Art, how many of them are also funding the Sesame Street Autism Project” (None at all, 57 very few, a moderate amount, very many, all of them). As expected, those in the high 58 affiliation condition (3.64) perceived more than those in the control affiliation condition 59 (3.23), (F(1, 100) = 3.13, p = .08). 60

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2 Author Accepted Manuscript 3 Experiment 2: Additional Details 4 5 First, participants were told to “Imagine for a moment that you have $50 to fund one of the 6 following projects. Which one would you choose? (You can only choose one.)” 7 8 They were then shown screen shots for four ideas from which to choose: 9 10 1. 11 12 13 14 15 16 17 18 Peer Review Version 19 20 21 22 23 24 25 2. 26 27 28 29 30 31 32 33 34 35 36 37 38 39 3. 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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2 Author Accepted Manuscript 3 4. 4 5 6 7 8 9 10 11 12 13 14 15 16 17

18 Peer Review Version 19 20 21 Next, all participants read about an idea seeking funding: 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Sealand Eco Collection is a selection of bags & accessories to perfectly match your urban 36 37 outdoor lifestyle. The entire collection is produced from Recover®, an environmentally 38 responsible fabric, made from a high quality blend of upcycled Cotton and post-consumer 39 recycled PET bottles. 40 41 This collection boasts an environmental consciousness, long lasting quality 42 and multi-functionality. 43 44 45 Participants in the high affiliation condition read: “A large number (> 50%) of the people 46 funding [project chosen], the first project you funded, are currently funding this 47 project.” 48 49 Participants in the control affiliation condition did not receive any additional information 50 regarding the overlap in funding between the two ideas. 51 52 53 We conducted a pretest on Amazon Turk with 151 North American residents (M Age = 36.73 54 years, 51.7% women; 31.8% of whom have previously funded an idea on an online 55 crowdfunding platform) to confirm the effectiveness of the affiliation manipulation. 56 Participants saw the same ideas and manipulations and were asked: “Of the people funding 57 [project chosen], how many of them are also funding Sealand Eco?” (None at all, very few, a 58 moderate amount, very many, all of them). As expected, those in the high affiliation 59 60

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2 Author Accepted Manuscript 3 condition (3.53) perceived more than those in the control affiliation condition (2.99), (F(1, 4 5 149) = 27.33, p < .01). 6 Experiment 3: Additional Details 7 All participants read about Sealand Eco as in the previous experiment. Next, all participants 8 9 read about diveLIVE, a second idea seeking funding: 10 11 12 13 14 15 16 17 18 Peer Review Version 19 20 21 22 23 24 25 26 27 28 Those in the more communal condition read the following project description: 29 30 31 Let’s learn about the oceans 32 33 This is why we want to launch diveLIVE, a pioneering LIVE virtual diving experience. We 34 believe that if we can get people to understand the oceans, they will all be encouraged to 35 think about them more often. 36 37 Our mission is to make history because the ability for divers to talk underwater while 38 39 streaming LIVE video to the internet was limited. Until now that is, as we have developed 40 technology and systems that will enable exactly this. 41 42 Through diveLIVE, we now want to do the same for the oceans. We aim to keep doing this 43 indefinitely. diveLIVE is not a once-off or short-term endeavor but set up to be a long-term 44 and sustainable way of researching the oceans. 45 46 47 Those in the less communal condition read the following project description: 48 49 This product uses technology to take videos of the oceans 50 This is why we want to launch diveLIVE, a pioneering LIVE virtual diving experience. We 51 52 believe that if we can get people to use this platform, they will all be encouraged invest in 53 this business opportunity. 54 55 By investing in this platform, our technology will be making history because the ability for 56 divers to talk underwater while streaming LIVE video to the internet was limited. Until now 57 that is, as we have develop technology that will enable us to do exactly this. 58 59 60

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2 Author Accepted Manuscript 3 Through diveLIVE, we now want to do the same for our investors. We aim to keep growing 4 5 indefinitely. diveLIVE is not a once-off or short-term endeavor but set up to be a long-term 6 and sustainable business venture. 7 Affiliation was manipulated just as in experiment 2. Participants in the high affiliation 8 9 condition read for each: “A large number (> 50%) of the people funding Sealand Eco, the 10 first project you funded, are currently funding this project.” 11 12 Participants in the control affiliation condition did not receive any additional information 13 regarding the overlap in funding between the two ideas. 14 15 16 Figure W9. Effect of Affiliation and Idea Description on Funding (Experiment 3) 17 18 Peer Review Version 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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