تاثیر اطلاعات آنلاین در تصمیم گیری در مورد سرمایه گذاری مبتنی بر پاداش مبتنی بر مخارج  The influence of online information on investing decisions of reward-based crowdfunding

1. Introduction In recent years, crowdfunding has become a valuable alternative source of funding for entrepreneurs seeking external financing. It is an emerging approach for entrepreneurs to implement their ideas despite not having traditional monetary resources such as banks and venture capital. Through crowdfunding platforms, the crowd can invest in business ideas and projects, and entrepreneurs can raise funds via the Internet. According to a report from massolution.com (2013), global crowdfunding experienced accelerated growth in 2014, expanding by 167% to reach 16.2 billion dollars, up from 6.1 billion dollars in 2013. In 2015, the industry is set to more than double once again; it is well on its way to raising 34.4 billion dollars. Using one of the most popular reward-based crowdfunding sites, kickstarter.com, N3.5 million people from nearly 20 countries on Earth pledged over 2.47 billion dollars to bring 108,437 creative projects to life, from the date kickstarter.com established till now. In China, crowdfunding sites emerged in 2013 and as of the end of 2014, the number of crowdfunding platforms was over 115 and over 0.9 billion Yuan had been raised using them. ver 115 and over 0.9 billion Yuan had been raised using them. Depending on what investors receive for their contributions, the categorization of crowdfunding platforms has four main types: donationbased, reward-based, lending, and equity (Hemer, 2011). Prior studies have investigated all four kinds of crowdfunding platforms from different perspectives: Meer (2014) used data from a donation-based crowdfunding website to estimate the effect of price efficiency on giving, suggesting that price efficiency plays a crucial role in donation crowdfunding project performance and that competition plays an important role in the market for donations. Mollick (2014) summarized a description of the underlying dynamics of success and failure among crowdfunded ventures based on a dataset of over 48,500 rewardbased projects. Those results suggesting that personal networks and underlying project quality are associated with the success of reward-based crowdfunding projects. Allison, Davis, Short, and Webb (2015) found that in lending crowdfunding platforms, lenders respond positively to narratives highlighting the venture as an opportunity to help others, and less positively when the narrative is framed as a business opportunity. In the equity crowdfunding context, Ahlers, Cumming, Günther, and Schweizer (2015) used signaling theory to examine the impact of firms’ financial roadmaps, external and internal governance, and risk factors on fundraising success. As we can see from the existing literature, most prior researchers tried to find how entrepreneurs who started various projects can raise more money in crowdfunding sites from a “creator’s” perspective. They do not provide a model of the formation of funders’ attitude toward a crowdfunding project nor how such attitudes relate to the funders’ online investing or funding decisions. Few studies explore how funders evaluate the content quality of crowdfunding project information. This limits our understanding of how online information about crowdfunding projects can be managed to increase the crowdfunding project success ratio. The elaboration likelihood model (ELM) is a major theoretical model used in online behavior research (Cheng and Ho, 2015; Chu and Kamal, 2008; Gupta and Harris, 2005; Ho and Bodoff, 2014; Shih, Lai, and Cheng, 2013; Park and Kim, 2008; Lee and Youn, 2009; Sher and Lee, 2009). In preceding literature, information about production quality and specifications is always classified as the central route, and the electronic word-of-mouth cues are the peripheral route (Cheng and Ho, 2015). Several researchers have explored the influence of factors related to these two routes on consumers’ final attitudes toward the product and willingness to purchase (Ho and Bodoff, 2014; Luo, Wu, Shi, and Xu, 2014; Lee, Park, and Han, 2008; Lowry et al., 2012). However, few studies explore the effect of the two routes of ELM on decisions to invest in a crowdfunding context. As said in former chapter, the categorization of crowdfunding platforms has four main types, the process complexity and risk varies greatly in these four different categorizations. In donation-based crowdfunding platforms, investor join crowdfunding activities without desire to get rewards, they donate their money and time due to sympathy and empathy factors (Gerber, Hui, and Kuo, 2012; Meer, 2014). In donation-based crowdfunding context, the process complexity and risk are both very low, investor act like donator (Hemer, 2011; Gerber et al., 2012; Meer, 2014), so we cannot implement ELM model in donation-based crowdfunding research. Contrast to donation-based crowdfunding, the process complexity and risk are much higher in lending and equity crowdfunding, investors always face much more information and have much deeper consideration (Hemer, 2011; Joenssen, Michaelis, and Müllerleile, 2014). In some lending and equity crowdfunding platforms, platform provide due diligence service to online investors. Meanwhile, some investors require creators provide project finance roadmap (Ahlers et al., 2015; Magdalena and Bart, 2015). All of these illustrate that the decision process is very complex in lending and equity-based crowdfunding context, investors have different perception path and behavior patterns in different crowdfunding context. In prior literature, some researchers have figured out investors always act like consumers in reward-based crowdfunding platforms, because the major business model of reward-based crowdfunding is “pre-selling” (Hemer, 2011; Mollick, 2014; Massimo, Chiara, and Cristina, 2015; Magdalena and Bart, 2015). When investors considering whether to fund these “pre-selling” project, their online behavior just like consumers buy goods (Hemer, 2011; Mollick, 2014). So, in reward-based crowdfunding context, we can use ELM to investigate factors affecting the investment decisions about reward-based crowdfunding projects. Potential factors affecting funders’ decisions are classified into one of the two routes. Based on previous literature, this study defines the signals of project quality as the central route and electronic word-of-mouth as the peripheral route in assessing the investors’ attention to the two routes and the routes’ influences on investment decisions.