A Link to New Product Preannouncement: Success Factors in Crowdfunding
Corresponding author: Dieter William Joenssen Department of Quantitative Methods, University of Technology Ilmenau, Ilmenau, Germany Email:
[email protected] Telephone: +49 (0) 36 77 - 69 40 68
Co-author: Anne Michaelis Department of Marketing, University of Technology Ilmenau, Ilmenau, Germany Email:
[email protected] Telephone: +49 (0) 36 77 - 69 40 80
Co-author: Thomas Müllerleile Department of Service Information Systems Engineering, University of Technology Ilmenau, Ilmenau, Germany Email:
[email protected] Telephone: +49 (0) 36 77 - 69 40 88
Electronic copy available at: http://ssrn.com/abstract=2476841
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A Link to New Product Preannouncement: Success Factors in Crowdfunding Abstract Crowdfunding is a process where commercial or non-commercial projects are initiated in a public announcement by organizations or individuals to receive funding, assess the market potential, and build customer relationships. Crowdfunding is employed in a variety of different categories, ranging from technology to arts and even disaster relief. Even though crowdfunding has received widespread attention, not only in the media, literature on the subject is still sparse and lacks especially empirical research. In contrast, research in new product preannouncement is established and more mature, and, due to similarities in process and goals, yields entry points for crowdfunding research. This paper contributes by reducing the gap in crowdfunding research by drawing on insights from new product preannouncement literature. To this end, a common definition of crowdfunding, which is currently lacking in literature, is derived and used to characterize commonalities with new product preannouncement. This theoretical discussion is complemented by empirically testing the derived hypotheses about common success factors. Conclusions are drawn from the logistic-regression, using the technology category of a project dataset with 45,400 observations. Research shows that while timing and communication are key success factors, common to both new product preannouncement and crowdfunding, other success factors may already be standard and cannot separate the successful crowdfunding projects from the unsuccessful. Keywords: Crowdfunding Definition, New Product Preannouncement, Customer Interaction, Empirical Research
Electronic copy available at: http://ssrn.com/abstract=2476841
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Introduction In the past few years crowdfunding (CF) has become an increasingly accepted complement to traditional financing models. Since the inception of Kickstarter, the current market leader, over 988 million dollars have been pledged for more than 130,000 projects in 13 different categories from more than 5.5 million people (Kickstarter, Inc. 2014). As there are no intermediaries like financial institutions, which can refuse to support a project idea, successful project financing hinges only on engaging sufficiently many people to contribute money directly. Furthermore, project initiators receive direct feedback from the crowd on their project and may hence better estimate their idea’s market potential. CF dynamics, as well as geographic crowd dispersion, enables project initiators to overcome financing barriers and utilize globalization for successful financing. In the context of marketing, CF gains relevancy by enabling project initiators to promote and market their project idea, akin to a traditional communication campaigns, at greatly reduced costs. Despite its growing importance, the topic of CF remains relatively unexplored in literature to date. There are few scientific papers that deal exclusively with CF such as Agrawal et al. (2011) and Belleflamme et al. (2011). Literature streams that aim to provide a theoretical background for CF refer mainly to literature on donation or open innovation (e.g., Ordanini et al. 2011). But these theoretical approaches are only able to explain the complex and dynamic mechanisms at work to a limited extent, because CF specific features are only partially covered. The few empirical investigations that may be found in literature focus on heterogeneous topics. For example Agrawal et al. (2011) show that the local and distant crowds differ in terms of funding decision timing. Ordanini et al. (2011) reveal that behavior patterns of the crowd differ, depending on the project category. Kuppuswamy and Bayus (2013) also investigate pledgers and project initiator behavior. They show that potential pledgers feel responsible to contribute to a project that has not received much support and stipulate that update-
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frequency from project initiators increases towards the end of a funding period. Factors influencing funding success have, so far, been neglected in the context of project CF. In order to ameliorate this neglect, this study investigates the impact of various factors on the CF project success. These relevant success factors are identified and their selection is justified by drawing upon established new product preannouncement (NPP) literature. Following this, the impact of the identified factors on funding achievement is evaluated empirically through the results of a logistic-regression-analysis performed on data comprised of all projects in the category technology, from the last 14 months, in a dataset of 45,400 CF projects from Kickstarter. The contributions by this study are threefold. First, the CF concept is analyzed yielding a comprehensive definition that broadens existing definitions and the understanding of CF. Second, relevant factors, which may influence successful financing of projects seeking CF, are derived by drawing on findings from NPP literature. The comparison of CF with NPPs reveals several intersections that provide valuable insights for CF. Third, a contextual contribution is provided by empirically exploring the impact of the identified factors on funding success of CF projects. Based on these empirical results, actionable recommendations for increasing funding-success-probability are formulated and discussed. Overall, this study will add to the dearth of research on CF and broaden the understanding of its success factors. The remainder of this paper is organized as follows. In the next chapter, the intersection between CF projects and NPP is characterized. Following this, relevant success factors are identified and hypotheses are derived by drawing on NPP literature. Then, identified success factors are empirically tested. The paper concludes by discussing the results and their implications for CF projects, in the context of NPP.
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Comparing Crowdfunding to New Product Preannouncement Defining Crowdfunding Currently there are several definitions for CF available in web-based and print media articles. However, definitions of CF are still rare in scientific journals (Hemer 2011). Existing definitions focus on a specific CF issue and draw predominantly on microfinancing, donation or crowdsourcing aspects (e.g., Belleflamme et al. 2011; Ordanini et al. 2011), and are hence too narrow on the overall CF concept. For example, Ley and Weaven (2011, p. 86) consider CF from a venture capital perspective and defines it as a “(…) source of start-up equity capital pooled via small contributions from supporting individuals collaborating through social media.” Other studies (e.g., Giudici et al. 2012; Hemer 2011) rely on a more comprehensive definition provided by Belleflamme et al. (2011, p. 5,6), who define CF as “(…) an open call, essentially through the internet, for the provision of financial resources either in form of a donation or in exchange for some form of reward and/or voting rights”. However, these definitions fall short as they do not include all relevant CF aspects, for example they neglect to take pledger motivation into account and why CF projects are initiated. A systematic overview, useful for gaining a comprehensive understanding of the CF concept and relevant aspects, based on deliberations, the available data at hand, and existing literature (e.g., Cellan-Jones 2013; Hemer 2011; Ordanini et al. 2011), is given in Table 1. This overview builds the foundation for a comprehensive definition and presents the aspects where, who, what, why, when, communication, and pay-out methods. --- Please insert Table 1 about here --Previous studies focus on CF taking place online (e.g., Ordanini 2011). This constraint is not warranted, because although there are several online platforms that facilitate the transfer of information and financial resources, such as Kickstarter, Indiegogo, Spacehive, and Kapipal, the concept of CF is not confined to the internet. For example, locals from Lancashire,
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England, not only pledged funds to make a broadband internet connection available, but also volunteered and dug the required trenches (Cellan-Jones 2013). A project of this type must surely be covered by any CF definition. CF involves project initiators on the one hand, and pledgers on the other. While project initiators can be individuals or organizations, pledgers are mainly individuals. Both groups are motivated by different factors for their involvement in CF. Project initiators are given the opportunity to realize project ideas, which may fall into such heterogeneous categories as art, music, nutrition, or technology. Further, the initiators can assess the ideas market potential, not only through size and amount of pledges, but also through communication with the pledgers. This possibility for feedback may be used to establish positive customer relationships and reputation, not only on a project, but also on a product level. In an example of this, Ouya Inc., in their second Kickstarter projects, sought to finance game developers for the Ouya game console, which was financed in an earlier project on the same platform (Henderson 2013). Positive reputation gained through the first project will facilitate funding by mobilizing pledgers to contribute again. But the reasons for pledgers to participate are also manifold. First, their small monetary contributions towards the overall goal may be compensated by the initiators through material rewards. For example, the Pebble smart watch project that started the wearable technology trend, mainly offered different variants of the watch as a reward. Second, pledgers may be compensated with an immaterial reward. This can be either directly related to the project or not. For example, the only other reward for the previously mentioned project was to receive the pledger exclusive updates. These rewards, whether material or not, often depend on the pledged amount. The watch, in its most basic color, required a pledge of $115, whereas simply receiving the pledger exclusive updates only cost $11. A third reason for participation may be found in the intrinsic gratification received by supporting a project. 1
A reward chosen by 2615 persons.
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Ordanini et al. (2011) refer to this motivation and find that pledgers may be guided by feeling involved. This motivation for pledging applies especially for the music and social categories, where a project may be non-commercial in nature, such as a wedding trip. The timeframe for promoting a project and raising money can be freely set by the project initiators. The timeframes for funding on Kickstarter can range anywhere from one day to two months and beyond, with a mode of one month. During this timeframe the CF platforms facilitate communication between project initiators and pledgers by providing a comment function. Communication and the interest to stay connected is not only a motivation for pledging, but also for pledging at a certain level. While Kickstarter requires a pledge to be made before being able to comment on a project, private status updates after financing may be offered as immaterial reward, as was done in the Pebble project. Initiators start their projects with an “open call”, a public announcement to pledgers as crowd, and project is successful funded when the monetary goal, set prior to the open call, is achieved or exceeded during the funding period. On some platforms, such as Kickstarter, pledgers are refunded the pledged amount if the funding goal is not achieved. On other platforms, such as Indiegogo, projects can be partially financed, i.e., a pay-out occurs even if the goal is not achieved. Thus, the pay-out scheme represents a platform specific attribute, and does not constrain CF as a concept. Based on this review, the following comprehensive definition for CF is proposed: Crowdfunding is a process where commercial or non-commercial projects are initiated in a public announcement by organizations or individuals to receive funding, assess the market potential, and build customer relationships. Pledgers may then contribute individual amounts of monetary or non-monetary resources, during a specified time-frame, using offline or online campaign platforms that utilize different payout schemes, in exchange for a product specific or unspecific, material or immaterial reward.
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New Product Preannouncement Definition The concept of NPP is more mature, but nonetheless a plethora of definitions, with in part subtle differences, exist. A general definition is given by Eliashberg and Robertson (1988, p. 282), who define NPP as a “(…) formal, deliberate communication before a firm actually undertakes a particular marketing action such as price change, a new advertising campaign or a product line change”. Further definitions build on this and vary in aspects such as the addressed target group, preannouncement timing, product availability, and strategic nature of NPP. Burke et al. (1990, p. 342) consider the consumer related definition: “[NPP is the] formal communications that provide new information to consumers about a product's availability, features, applications, defects, or its discontinuation”, which this paper follows for reasons apparent through the following discussion. In contrast to advertising, which promotes a product already available, NPP promotes a product before it is actually available on the market. Timeframes range from a few days up to two years (Eliashberg and Robertson 1988; Kohli 1999; Robertson 1993). A further distinction is offered by Koku et al. (1997), who distinguish between preannouncement and announcement. They stipulate that preannouncements are made far in advance of the launch date, whereas announcements are made close to product launch. A threshold for a more accurate temporal differentiation is lacking and remaining literature on the subject prefers to distinguish between late and early preannouncements. NPP are usually made by established firms in technology-driven industries such as computer hard- and software, telecommunication, photographic equipment and automobile (e.g., Calantone and Schatzel 2000; Koku et al. 1997; Wu et al. 2004). Next to competitors, investors, distributors or the sales force, potential customers are the most important target group for a firm’s NPP (e.g., Eliashberg and Robertson 1988; Hoxmeier 2000). Reasons for businesses to preannounce new products are abundant. NPPs may be utilized to disseminate
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information on and to encourage interest in products to potential customers (Koku et al. 1997; Lilly and Walters 1997; Lee and Colarelli O’Connor 2003; Montaguti et al. 2002; Rabino and Moore 1989). This may also facilitate customers by allowing them to review the product before actually trying it (Farell and Saloner 1986; Kohli 1999, Montaguti et al. 2002). Thus, the adoption process is advanced and the perceived risk in purchasing is ameliorated, to an extent. Furthermore, potential customers are able to plan ahead and thus avoid switching costs (Kohli 1999; Montaguti et al. 2002; Pae and Hyun 2006), but these customers may also delay their planned purchases. This reduces the demand for currently available products (Farell and Saloner 1986; Lilly and Walters 1997). In addition to stimulate consumer demand, NPPs provide the opportunity to generate feedback from potential customers prior to product launch. In this sense, NPPs may serve as a test platform, for example different product designs may be benchmarked (Lilly and Walters 1997; Pae and Hyun 2006). Intersection Characterization of Crowdfunding and New Product Preannouncement After offering a definition of CF and considering definitions for NPP, properties that both exhibit are discussed to deduce insights from NPP, also applicable for CF. The addressed target groups of NPPs include several stakeholders, dependent on the planned strategic impact. The most important target groups are competitors and potential customers (e.g., Eliashberg and Robertson 1988). In contrast, CF projects are primarily directed at pledgers. These individuals, depending on the product category, may also be potential customers. For example, project initiators, who promote an idea in form of a product, often seek to further sell this product upon project completion. Thus, an essential component of NPPs is the promotional effect before launch, a property that is shared by CF projects, which applies especially to the category technology. Although several definitions provided for NPP refer to a “product” that is preannounced, the stage of product development is not specified. This clearly leads to the conclu-
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sion, that dependent on the stage of development, the “product” may be no more than a project idea that is preannounced, for which the parallels to CF are self-evident. A secondary goal for CF projects is to actively and emotionally involve the pledgers as potential customers (Steinberg and DeMaria 2012) in an early state. This corresponds to NPP aims to start the adoption process as early as possible, so that product interest and diffusion may be stimulated and enhanced, respectively. An important difference between NPP and CF lies in the fact that pledgers are not only emotionally vested in the success of the project, but also monetarily. NPPs are only relevant for potential customers’ present and future purchase decisions and thus no concrete monetary vestment is made. For pledgers the pledge, an action akin to purchasing the product, is performed before the product exists. Hence, the decision to pledge may be considered to constitute an inter-temporal consumption decision. Thus, a risk is taken by pledgers, which is not taken by potential customers targeted by a NPP. Nonetheless, the emotional investment for both the pledgers and the potential customers remains. For both groups the risk of beginning the adoption process for the expected product or waiting for the product in vain may is present. This risk may be perceived higher for CF projects, because the perception often is that CF is at an earlier product development stage. CF and NPPs can serve as feedback platforms. For both the ability to incorporate feedback, from either the pledgers or potential customers, is limited by the product or project development stage. CF or NPPs may be initiated at any development stage, both are congruent in regard to this property. The importance of communication, in all development stages, pertains to both concepts. NPPs must contain information about product functionality, availability, and usability (e.g., Popma et al. 2006; Talke and Colarelli O’Connor 2011). This information type is also communicated during CF projects, especially those referring to a product. In the sphere of both CF and NPP, the detail level and communication amount varies.
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The previously formed comparison between CF and NPP reveal several intersections, especially in terms of the primary idea that potential customers are informed about a new product which is not yet available on the market but will be in foreseeable future. However, whereas NPPs refer to potential customers’ purchase intention, CF refers to potential customers’ investment decisions in the first place. Though, as CF investment decisions are often linked to a subsequent purchase intention, sufficient justification to apply insights from NPP literature to CF is given, especially in terms of the timing, the amount of communicated information, and the target group. Since most definitions of NPP focus on a product being promoted (e.g., Sorescu et al. 2007; Wu et al. 2004), perhaps unduly so as mentioned above, the characterized intersection holds especially for CF in the category technology, where predominantly products are promoted and subsequently launched.
Conceptual Framework and Research Hypotheses The previous section shows that timing and communicated information are important factors for both CF and NPP, and that the intersection holds especially for the CF category technology. Using these findings, the conceptual model presented in Figure 1 is devised. It is proposed that timing and information level affect the performance of CF projects in the category technology, because, especially within this category, products are promoted. Timing and information level are particularly interesting because a large body of NPP literature would provide additional, valuable insights if these influences also exist for CF. --- Please insert Figure 1 about here --Effect of Timing on Product Performance Studies which address the timing of NPPs and its impact on the product’s success refer to enterprise-, industry-, customer- and product-related factors (Su and Rao 2010). In following,
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the focus is set on customer-related factors because of the parallels, which are self-evident, between pledgers of CF projects and potential customers. For NPP, “early” refers to preannouncements which are made long before product launch, whereas “late” refers to preannouncements made close to product launch (Lilly and Walters 1997). Studies on NPP timing argument that with increased customer learning requirements, products should be preannounced early (e.g., Eliashberg and Robertson 1988). This should apply especially to highly innovative products because potential customers have no experience and first need to seek out information in terms of product functionality, usability and how it distinguishes itself from existing products (Lilly and Walters 1997). However, since Lilly and Walters (1997) only provide propositions, which are not empirically tested, an argument may also be made for the converse. Especially when a product is highly innovative and no surrogate product exists, interest may be high for potential customers to use the product, say “tinker” with it, to learn about its functionality. Furthermore, pledgers have time to gather information and build an interest during the defined campaign timeframe, which at a median length of 30 days may be sufficient time. In addition, the monetary risk component, mentioned above, makes pledging similar to pre-purchasing. This may make CF projects that are started too early in the development cycle more prone to failure. Thus, argumentatively, the potential effect of timing on CF is not clear. Nonetheless, as an effect in either direction may be detected, the following hypothesis is derived: H1: A close estimated delivery date positively influences the project success. Effect of Information Level on Product Performance Previous studies on the content of NPPs address the type (e.g., Talke and Colareli O’Connor), the properties (e.g., Chen et al. 2007), and the amount of information (e.g., Chen and Wong 2012) that are communicated, and the influence on potential customers. The amount of communicated information in NPPs is particularly important because the information needs of
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potential customers increase, especially for innovative products (e.g., Guiltinan 1999). A NPP with a high information level is characterized not only by high amounts of information, but also by the amount of detail (Koku et al. 1997). A high information level positively influences the clarity of the NPP and, hence, contributes to a better understanding of the message (Chen and Wong 2012). Furthermore, studies indicate NPPs that provide a high information level are more credible (Sattler and Schirm 2002)). A high perceived credibility, in turn, positively affects the behavior of potential customers, because a high credibility reduces the perceived risk and uncertainty attributed to the new product (Brockhoff and Rao 1993; Kohli 1999). Sorescu et al. (2007) also show that NPP updating is crucial for the perceived credibility as updates inform about the progress of the new product. As the transfer to CF of these findings is quite straightforward, as the discussion in the previous chapter indicates, the following hypotheses are derived: H2.1: An increased amount of updates positively influences the project success. H2.2: The availability of a unique project website positively influences the project success. H2.3: The provision of more images influences the project success positively.
Methodology Sample The current market leader in CF campaign platforms, Kickstarter, was selected as a data source to answer the hypotheses. A total of 45,400 samples were collected between May 16th and 19th 2013. Data available publicly on Kickstarter was extracted by a custom web crawler. Variables collected included requested and pledged funding amount, time from the end date for the funding period to the expected delivery date, update count, project website, and the number of images on the project page. Upon completion, necessary data transformation was performed to yield variables suitable for statistical analyses. Transformations included deriv-
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ing whether the given project website is unique, whether a project was successfully funded, and the amount of months from the end of the funding period to the expected delivery date. After data collection and transformation, it was deemed necessary to constrain the analysis to projects initiated in the past 14 months. This constraint was implemented due to the increased attention CF received at that time, which resulted in a structural change in the market. Based on this constrained dataset, further data cleaning was performed. First, four projects with missing data, namely missing expected delivery dates, were excluded from the sample. Further, projects regarded as outliers, i.e., those not representative of normal projects, were detected and eliminated. Projects deemed not representative of normal projects were those with excessive funding wishes2 and excessive amounts of comments and pictures3. Also, projects abandoned4 by their initiators were removed. Application of these criteria yielded 37,745 projects, of which all 1,042 technology projects were extracted for analysis. Again, technology products were selected because of their inherent economic characteristics. Most projects in this category are physical products without any intangible parts, i.e., services, and are therefore sufficiently similar to products in the NPP context. Analysis Answering the chosen hypotheses requires determining how the independent variables influence the probability of successful project funding. Since project success is a binary variable5 and the probability of success is constrained to values between zero and one, a proper method for analyzing the exogenous variables’ magnitude of influence is a logistic-regression-model. Since timing and the amount of communicated information are identified as, theoretically, crucial success determinants, the regressors are selected as follows: the months to de 2
Non-serious campaigns like the “Death Star” project (www.kickstarter.com/projects/461687407/kickstarteropen-source-death-star) were identified and excluded. 3 Some projects used picture tessellation. These projects were eliminated because picture tessellation dramatically increases the amount of pictures without increasing the amount of content. 4 Projects are considered abandoned if they received no pledges, no comments or no updates were made. 5 A project on Kickstarter is considered successful, if the funding goal is met or exceeded by the pledges. Only if this criterion is met are the pledges collected from the pledgers and transferred to the project initiators.
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livery (x1), the number of updates (x2) and images (x3) posted by the project initiator and whether or not a unique project-website (x4) was made by the project initiators. These variables may readily be influenced by project initiators, thus, any recommendations made upon the results will be actionable by practitioners. Based on these selections, the appropriate logistic-regression-model is as follows: P success 1|d
4 ed d with i ∙ xi . 0 1 ed i 1
As with any regression analysis, multicollinearity may pose a serious challenge for coefficient estimation in logistic-regression. Literature on the subject suggests calculating the tolerance for each independent variable or investigating the Pearson correlation between the independent variables (Menard 1995). For the tolerance, .2 is the threshold below which multicollinearity is a cause for concern; the Pearson correlations must not be “too large.” In the collected sample, the tolerance for months to delivery, the number of updates and images and whether the project has a unique website is .988, .968, .967, and .996, respectively. Since all these tolerances are well above the threshold of .2 and the Pearson correlations are all close to zero, as shown in Table 2, problems associated with multicollinearity should not be an issue for coefficient estimation. --- Please insert Table 2 about here --Results Interpreting the results involves a three-step approach. After the assessment of coefficient significance and model fit, coefficients must further be interpreted. The relationship between the independent and dependent variables is nonlinear in the logistic-regression-model. The parameter estimation results, given in Table 3, indicate, the effects of “months to delivery” and “number of updates” are highly significant (p≈0), the effect of “number of images” is significant at a 5% significance level, and having a “unique project website” does not have a statistically significant effect on the project success probability. These results hold
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when using either the Wald z-statistic or testing the deviance from the null-model for each parameter, using a chi-squared test. The significance of the variable “number of images” should be considered cautiously. If the family wise error rate were controlled, say with a Bonferroni-correction, the statistical significance of “number of images” would need to be rejected at a 5% level (exact p-values being .038 and .036 for the Wald and deviance statistics, respectively). Therefore, hypotheses 1 and 2.1 can be considered supported, while hypothesis 2.3 is not supported by these results. Hypothesis 2.2 may be considered unsupported by the data if the statistically more stringent methodology is applied. --- Please insert Table 3 about here --Model fit, in the context of logistic-regression, is considered based on deviance to the null-model. Various measures of model fit exist, prominent amongst them are McFadden’s (1979) and Nagelkerke’s (1991) R2 statistics. McFadden (1978) states that a pseudo-R2 of more than .2 indicates a good fit and over .4 indicates an excellent fit. Nagelkerke’s R2 is standardized to between zero and one and thus rules of thumb for regular regression analysis may be applied. McFadden’s and Nagelkerke’s R2 for the model are .345 and .502, respectively, indicating a very good model fit. Coefficient interpretation for the current case is not only hampered by the nonlinear link function between the independent variables and the dependent variable but also by the mixture of metric and dichotomous variables. Thus, a comparison in change of success probability due to a marginal change in an independent variable offers valuable insight (Long 1997). To this end, values of a mean project and changes in success probability due to a variation of success factors are shown in Table 4. Mean project values, changed by one marginal unit, ceteris paribus, are two months to delivery, seven project updates, nine images, and a unique project website having a predicted success probability of 38.14%. As the values indicate, changes in success probability are nearly linear around the mean project. Changing the
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amount of months to delivery changes the success probability by about 4 percentage points, while adding one meaningful update increases success probability by about 7 percentage points. The overall effect of adding one more image is small with less than one percentage point decrease in the success chances. --- Please insert Table 4 about here --Conclusions The goal of this paper is to not only contribute theory to CF literature, which is scant to date, but also to provide empirical evidence of which factors are critical to CF success. These contributions are threefold. First, and most notably, this is the first study to offer a comprehensive definition of CF, including various dimensions neglected by previous definitions it is built upon. Second, success factors are identified, facilitated by drawing on insights from NPP literature. The results show that especially NPP literature on customer orientation, informational design and timing is relevant for the CF literature. Third, the identified success factors are empirically tested using logistic-regression-analysis. This analysis is the first of its kind, and thus contributes uniquely to the understanding of a new way to engage customers. The analysis of the 1,042 recent technology projects shows CF projects executed closer to their estimated delivery date have a higher probability of successful funding. This result is consistent with existing NPP literature, which suggests that the NPP, in the technology sector, should take place closer to launch (Kohli 1999). However, the estimated delivery date should be set realistically close to the end of the funding period. Not only do NPP studies show that the credibility of the preannouncing company is negatively affected when communicated launch dates are delayed (Kohli 1999), but also common sense dictates that delivery dates set too aggressively lose credibility. Further, results of this unique empirical investigation show that increasing the number of updates has a positive effect on project-success-probability. Updating is crucial to inform
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about the development progress and shows project initiator commitment. However, the number of updates should be appropriate to the project and of comparable quality. Simply separating one update into two or adding an inane update should not increase the success probability. Final findings are that the number of images provided and the availability of a unique website could not be confirmed to have an influence on project success. Explanations for this may be found in the high rate of projects within the technology category that offer a unique project website. Of the 1,042 projects investigated, about 92% of all projects had a website, of which about 74% are unique. Thus, having a unique website does not constitute a sufficient competitive advantage. Further, this may also aid in explaining why the number of images is not a significant success factor. Images may be predominantly published on the associated project websites, which were not surveyed. Then again, depending on the project stage, artwork, or rather more artwork, may not offer more information than is already contained within updates or the project idea. However, some limitations are worth noting. In this study only one category, technology, which normally offers tangible rewards, was assessed. Results for other categories may yield different outcomes. In the statistical model only quantifiable variables were used. Other success factors, especially the idea itself, the initiators reputation, and notoriety are not quantitative in nature and therefore not directly measureable. In general only funding success probability is investigated, but other measures of success, such as stretch goal achievement or overfunding, could be considered. In future work, other aspects of CF, such project completion and the effect of missed delivery dates, could be investigated utilizing NPP theory.
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Table 1. Overview Crowdfunding Concept Categories
Dimensions Online
Offline
Who?
Initiator - Individuals - Organizations
Pledgers - Mainly individuals
What?
Commercial projects
Non-commercial projects
Material reward
Immaterial reward
Where?
Why pledge?
Why initiate?
Project realization
When?
Defined campaign timeframe
Product related Non-product related Act of support
Assess market Potential
Communication
Unilateral
Bilateral
Pay out scheme
Winner takes it all
Pay out always
Figure 1. Research Framework for Technology Crowdfunding Projects
Build customer relationship
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Table 2. Pearson Correlations Between the Independent Variables months to delivery number of updates number of updates
-.080
number of images
-.082
.163
unique project website
-.030
.042
number of images
.049
Table 3. Logistic-Regression Results coefficient
exponentiated coefficient
Wald z-statistic
Deviance statistic
(intercept)
-1.750
−−
-8.377***
−−
months to delivery
-.157
.854
-3.366***
23.52***
number of updates
.291
1.337
14.955***
454.97***
number of images
-.028
.973
-2.077*
4.39*
unique project .086 1.089 .480 website Significance codes: ‘***’ p < .001; ‘** ’ p < .01; ‘* ’ p < .05; ‘ ’ p < 1;
.18
Table 4. Mean Case Sensitivity months to delivery number of updates number of images unique project website -1
.038
-.066
.007
-.020
+1
-.036
.071
-.006
−−
