Crowdfunder’s Paradox and Herding dynamics in Reward Based Crowdfunding

Crowdfunder’s Paradox and Herding dynamics in Reward Based Crowdfunding | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Crowdfunder’s Paradox and Herding dynamics in Reward Based Crowdfunding Subhash Karmakar, Gautam Bandyopadhyay, Jayanta Nath Mukhopadhyay This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8601520/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the interaction between quality signals and herding behaviour in reward-based crowdfunding and how this interaction generates information cascades. Using live campaign data from Kickstarter comprising 1,056 observations between March and April 2025, the findings reveal heterogeneous backer responses across campaign stages. In the early phase, informed backers rely on observable quality signals such as creator updates, project comments, and creator experience (prior projects created and backed). During the middle phase, these signals become critical in sustaining campaign credibility and driving progress toward the funding threshold. In contrast, late-stage backers exhibit strong herding tendencies and contribute based on accumulated funding momentum rather than signal quality. The study demonstrates that backers self-select into early or late contributors depending on their information-processing ability, leading to information cascades that reduce asymmetric information. The findings offer implications for campaign creators seeking strategic signalling and regulators aiming to protect uninformed contributors. JEL: G30, G32 Reward based crowdfunding Information cascade herding behaviour Signaling Figures Figure 1 Figure 2 1. Introduction A general tendency in today’s crowdfunding ecosystem is to identify factors that contribute positively towards success of the Crowd Funding (CF) campaigns. Literatures in this area contributes towards the growth of the crowdfunding ecosystem and promote innovation, where focus on sustainable innovation has opened a new vista of research. Entrepreneurs remain engaged in seeking new and innovative ways to attract investors or funders to fund their projects. The growth and proliferation of crowdfunding since 2008 have helped the entrepreneurs to collect small amount of funds without any hindrance, where there has been a shift from the traditional and sustainable channels of finance (Cillo et al., 2019 ). Given that this may bear a lot of constraints and obstacles, entrepreneurs are now looking forward towards Crowd Funding (CF). Therefore, CF has been a unique way of entrepreneurial finance, which is has its own nature and can be distinguished from other forms of financing Viz. angel investors, Venture capital etc. (Rossi et al., 2021 ). CF works on wide geographical areas via online platforms which supports the campaigns rolled out by the entrepreneurs, the “campaigner” or the “creator” of the project who generally comes out with an idea and the “backers “ or “funders” who ultimately contribute funds for the completion of the project. CF typically refers to a paradigm where merging of funds from various small investors that circumvents the normal funding sources and thereby meet the expectation of entrepreneurs and in the process crowd funders also enjoys the “community benefits”(Belleflamme et al. 2014 ; Moritz and Block ,2016). For our purpose, we have focused on Reward-Based Crowd Funding (RBCF) which has gained considerable importance for financing start-ups and novel concepts. In RBCF , the creator of a project introduces an idea or product on an online platform to attract backers who can help finance its development. The risk is inherently high because creators often have little initial knowledge of market acceptance, and backers lack certainty regarding the project’s eventual success. This model may also involve preselling products during early stages (Vismara, 2018 ). The distinct features of the RBCF provides ample scope and opportunities to distinctly study the role of information signals as well as their role in form of herding and information cascades at various stages of the campaign. Literature has shown the effectiveness of signals in defining the success of RBCF campaigns are campaign features—such as updates and comments that have shown a positive association with funding outcomes (Shenor & Vik, 2020; Hadacova & Malicka, 2022,Pocsh et.al.,2022). The role of funders are also important and a campaign, which can attract more number of funders, have a better likelihood of success as compared to others. In this context, it is very important to identify the role of herding and information cascades. Literatures have studied that signals play a critical role in influencing backer behavior. Studies have examined how signals—such as funding goals, timely updates, and clear descriptions, interact with herding behavior (Garnica et al., 2024; Shneor & Vik, 2020 , Comeig et al.,2020). These signals not only attract early backers but also help build credibility, influence subsequent contributions. In this paper, we have identified multiple quality signals such as number of comments by backers, number of updates by creators that requires regular monitoring to enhance the success predictability including how they are influencing the growth of funders. In addition to that, we have included the skill and experience of the entrepreneur in form of projects created and projects backed by creators also. Our paper differentiates from the rest of the literatures since we have paid attention to changes in the number of funders at specific intervals over the campaign period enforcing the herding dynamics. The previous studies have provided a wide range of evidences on herding behavior in crowdfunding, but it is not still clear that how herding occurs in RBCF particularly the nature and stage of campaigns. We attempt to fill this gap by investigating the dynamics of crowdfunding stage wise based on live campaigns . In addition to that, we have also utilized the projects created and backed by the creator related to their experience to reveal how they are influencing herding at the later or earlier stages of the campaign and attracting more funders in form of information cascades and herding. Another aspect discussed in the paper is related to use of Machine Learning (ML) methods in prediction of herding dynamics . The predictability of success in crowdfunding have been mostly inferred through traditional regression methods but recently ML methods such as Linear Discriminant Analysis(LDA) (Oduro et.al.,2022), boosted trees and neural networks have been used ( Elitzur et al.,2024). We have used ML techniques in form of LDA to predict the role of information cascades and herding at various stages of the campaign. In sum, our contributions towards crowdfunding literature has been three folds. First, we examine the different information signals with their distinct and specific roles at various stages of the campaign, which shall help in understanding the funding dynamics with reference to herding at various stages of the campaign. Second, we also infer to the role regarding experience and skills of the creator in form of projects backed and created to define relationship with attraction of funders. Thirdly, the use of ML methods to improve the prediction of herding. 2. Theoretical Framework and Hypothesis development 2.1 Theoretical background and Literature review Crowdfunding (CF) manifests in different forms depending on the expected rewards or returns for backers or investors. Reward-based crowdfunding (RBCF) typically involves project creators presenting new ideas or products on online platforms to attract funding for development, often through early-stage pre-sales (Vismara, 2016 ). Backers may be motivated by monetary returns, personal support, or intrinsic satisfaction (Hagawe et al., 2023 ). RBCF thus provides an important context to study behavioral phenomena such as herding, as well as the dynamics of success factors. Campaign success is most commonly measured by whether the funding goal is achieved (Colombo et al., 2015 ; Buttice et al., 2017; Anglin et al., 2018 ; Deng et al., 2022 ).Research on crowdfunding success has examined both campaign-specific factors and the role of early backers. Studies by Mollick (2014), Cordova (2015), Crosetto and Regner ( 2014 ), and Vismara ( 2018 ) emphasize campaign attributes, while Astebro et al. (2024) and Dao et al. ( 2024 ) highlight the creator’s influence. Specific determinants of RBCF success include project goals, funding targets, backer numbers, and pledge amounts (Mollick, 2014); platform and campaign-related characteristics (Kaartemo, 2017 ); and creator updates and backer comments (Hornuf & Schwienbacher, 2018 ). All stakeholders—including platforms, creators, and backers—play a critical role in campaign outcomes (Dai & Zhang, 2019 ). Additional contributors include external funding, the experience and age of project directors (Rachelva & Roosenboom, 2020), and whether the creator is a serial crowdfunder (Usman et al., 2020 ; Elitzur et al., 2024 ). The dynamics of funding across campaign stages remain a key area of investigation. Herding behavior, where backers imitate the decisions of others, has been widely documented in crowdfunding (Astebro et al., 2018; Chan et al., 2020 ; Zhang & Liu, 2012 ). Information cascades, where investors disregard private information in favor of early signals, are considered a subset of herding (Scharfstein & Stein, 1990 ; Spyrou, 2013 ; Aleksina et al., 2019 ; Kapounek & Kucerova, 2019) and often occur in early stages before being replaced by herding behavior as campaigns progress (Garnica et al., 2024). Information asymmetry between creators and backers is a fundamental challenge in crowdfunding. Backers may lack knowledge of the entrepreneur’s skills, product quality, or business operations (Huang et al., 2023 ; Ahlers et al., 2015 ). To mitigate this, project creators can signal quality through visible cues, influencing potential backers’ decisions (Belleflamme et al., 2014 ). These signals can relate to both the project and the creator (Courtney et al., 2017 ; Huang et al., 2022 ) and often build trust within the community, enabling others to follow early backers’ actions (Akerlof, 1970 ; Wohlgemuth et al., 2016). Such signaling generates information cascades that contribute to herding behavior. This paper examines herding dynamics in RBCF by focusing on two key aspects. First, it investigates how visible project and creator-related signals influence herding at different stages of the campaign. Second, it explores the transition from early-stage information cascades to later-stage herding, where backers may make suboptimal decisions by relying on early contributors. Understanding these interactions offers insights into the behavioral and informational mechanisms driving crowdfunding success (Garnica et al., 2024; Shenor & Vik, 2020). 2.2 Use of Machine Learning in Crowdfunding arena In crowdfunding research, machine learning (ML) methods have been increasingly applied to predict factors contributing to campaign success (Dhochak et al., 2022 ; Blohm et al., 2022 , Ferrati, & Muffatto, 2021 ). ML approaches are particularly useful for distinguishing between subjective and objective features affecting success (Wang et al., 2021 ) and often outperform standard binary logit models due to their ability to capture complex relationships among predictors (Elitzur et al., 2024 ). In this study, we employ Linear Discriminant Analysis (LDA) using Jamovi software to predict the success of equity crowdfunding campaigns (Elitzur et al., 2024 ). ML methods have also been used as robustness checks in prior research (Elitzur & Solodoha, 2021 ). Notably, LDA has not yet been applied to reward-based crowdfunding, and our study addresses this gap by examining herding-related factors at different stages of crowdfunding campaigns (Oduro et al., 2022 ). Based on the previously mentioned studies the dynamics of herding may be put as: 2.3 Hypothesis development Hence, following the theory proposed by Garnica et.al. (2024) and Vismara ( 2018 ) where information cascades generated by the signals by early backers and experience of the creators which are later on replaced by herding of late backers, we argue that herding may be less intensive during the early stages of the campaign when the main constraints are availability of information. The late backers in case of crowdfunding takes suboptimal decisions based on the comments of early backers and updates by the creators. We therefore hypothesize that: H1: Herding occurs during the early stages of a campaign The second hypothesis is : H2: Herding occurs during the later stages of the campaign 3. Methodology and Data 3.1 Data The sample contains 176 campaigns from the Kickstarter platform with 1056 observations over 29 days of the campaign starting from Day 1 to Day 29. Our sample size is similar to that of data sample used by Dao et al.(2024).We have collected the data during various periods of the campaign such as first day, fifth day, tenth day, twentieth day, twenty fifth day and twenty ninth day. The projects in our sample includes all the categories (Arts,crafts,comics,dance,design,fashion,film,food,games,journalism,music,photography,publishing,technology and theater).The data was captured in ongoing campaigns during March2025 and April 2025.We could obtain some basic features of the campaign such as number of funders, number of updates by creators, number of comments by backers, number of projects created and backed by the creator. Measuring the herding momentum In line with the prior literature available (Jiang et al., 2018 ; Dao et al., 2024 ) the herding momentum in crowdfunding is based through sequential correlation of the number of funders. Under these circumstances, the information on total number of funders, which is available on the platform, shall attract more funders if herding exists ( Dao et al., 2024 ). In this approach the Log of daily investors at time t are examined with the Log of lag of daily investors at time t-1 and regression has been run. Log DailyInvestors jt = α 0+ ꞵLogLagInvestor jt-1+ƳXJT+ e jt Where j is the number of the listed projects, t = 2,……., T, and XJT includes a number of campaign specific variables. In this case the funding period is divided into sub-periods in which the first covers from the first day to middle day of the campaign and the second set with rest of the days. Herding is specified in the first set of days if ꞵ is positive and significant in the first part and the converse is true if it is ꞵ is positive and significant in the second part. However, this approach is based on the assumption that the number of previous investors will not decrease over time, which may not be applicable to reward based crowdfunding where any backer may pull out from the campaign at any time before the campaign closes. Various literatures have approached the role of herding in RBCF where there is role of early backers opinion (Petit and Writz ,2022 ; Comeig et.al. 2020 ; Kapounek and Kucerova, 2019) and the herding thereafter is observed at the later stages of the campaign. Another important phenomena observed in herding is related to the fact that prior funding of the campaigns with inverted U shaped effects and it is further strengthened by frequent updates(Zhu et al.,2022) According to prior literatures (Garnica et al., 2024, Vismara, 2018 ) herding is observed at the later stages of the campaign and information cascade at the early stages (Garnica et al., 2024). Accordingly, to measure the backer’s behavior the time has been considered as an important factor such as time to reach 10% of funding goal over the total duration of the campaign and a low value indicates early funding (Garnica et al., 2024). Herding increases the number of funders and accordingly we have number of funders as dependent variable (Jiang et al, 2024, Chan et al., 2020 and Dao et al., 2024 ). Considering the fact that and constraints where in RBCF campaigns backers may pull out from the campaign, we adopt an approach based on herding in financial markets where Bouri et al.(2019) found that herding behavior can be explained by presence of uncertainty using Logistic Regression. Several authors in case of cryptocurrency have further extended this theory where Logistic regression have been used to confirm the results related to herding dynamics (Amirat and Alwafi, 2020 ). We use this approach to get a view how information signals play a particular role in herding and to understand the influence of various factors, which are contributing towards herding at various stages of the campaign. To understand the temporal dynamics of herding we follow this approach where log of the odds have been examined with dependent variable as the total number of funders at day t of a campaign j and independent variables as number of total funders at day t-p (lag days/previous days), where p refers to previous days. If log of odds is significant and more than 1, the independent variables play a significant role in total number of funders and higher this odds ratio greater is the herding which explains the dependent variables are influenced by funders at different campaign lag periods (Dao et al., 2024 ). Herding takes place in the early stages if log of odds of the total funders at t and total funders at t-p is significant and more than 1 with high R-square value and H1 is accepted. In case herding takes place at later stages then H2 is accepted. Accordingly, we run logistic regression for first set of days (day 5 to day 20) and next set of days (day 25 and day 29). We use the following model to examine the herding behavior: In this model, the relationship between Z and the probability of the event of our interest is given by logit function or log of odds. P i = e Zi /1 + e Zi = log(P i / 1− P i) P i is the probability of ith event Zi is the value of value of the dependent variable The value of Z, which is the odds ratio, is expressed as Log Daily funders t (z i ) = α+ꞵ 1 *daily funders tj−p +ꞵ 2 *FAQ t−p +ꞵ 3 *update count tj−p +ꞵ 4 *comments tj−p + +ꞵ 5 *projects backed by creator tj−p + +ꞵ 6 *projects created tj−p e tj−p If log of daily number of investors at time t, which is the independent variable is the log of number of accumulated funders from the beginning of the campaign and up to day t-p then herding takes place. Accordingly, if herding takes place at earlier stages then H1 shall be accepted and it occurs at stages later stages then H2 shall be accepted. As discussed in literature (Bouri et al., 2019 ), we approach in the following manner, where we find out the odds ratio (Log of odds) where odds ratio is calculated with funders accumulated upto that period with total number of funders in the preceding period. We have segregated the periods as early period, which includes Day1, Day5 and Day10, and the late period includes Day 20, Day 25 and Day 29. In case the odds ratio is more than 1 and significant with a higher R-Square and Chi Square, preceding funders in the are influencing the accumulated funders and it shows herding, otherwise herding is absent. Another important feature where Odds ratio is calculated involves the same pattern of log of accumulated funders at various timelines as dependent variable with various proxies as discussed in literature such as number of updates, number of FAQs, number of comments and projects created and backed by the creators. In this case, if odds ratio is more than 1 and significant then it signifies the role of various factors in the growth of funders. We may represent the hypothesis as: 3. 2 Variables under study 3.2.1 Dependent variables Dependent variables refers to the number of funders at different periods starting from Day5, Day10, Day 20, Day 25 and Day 29. Since, funders are in numbers we have segregated the funders in categories such less than 40 funders, more than 40 funders but less than 60 funders, more than 60 funders but less than 80 funders, more than 80 funders but less than 100 funders and more than 100 funders. 3.2.2 Independent variables We have classified the independent variables into Observable actions in the project that are easily visible to others connected to the project which includes total number of funders in the preceding period ,number of Frequently Asked Questions(FAQs) available on the web page of campaign, number of campaign updates by creator and number of comments by backers. Experience of the creator that are available in the campaign web page such as number of projects backed and created by the creators. 4. Empirical results The results related to herding and descriptives have been presented, 4.1 Descriptives Table 1 Descriptives related to campaigns at various days Variable Mean Median SD Min Max Funders (Day 1) 48.74 6.50 127.31 0 1051 Funders (Day 5) 110.71 29.50 269.39 0 2419 Funders (Day 10) 121.72 30.50 317.72 0 2976 Funders (Day 20) 158.60 35.50 452.76 0 4827 Funders (Day 25) 178.11 39.50 528.96 0 5802 Funders (Day 29) 216.02 47.00 670.79 0 7578 FAQ Count (Day 1) 2.31 0.00 4.68 0 33 FAQ Count (Day 5) 2.52 0.00 4.80 0 33 FAQ Count (Day 10) 2.52 0.00 4.77 0 33 FAQ Count (Day 20) 2.64 0.00 4.84 0 33 FAQ Count (Day 25) 2.68 0.00 4.88 0 33 FAQ Count (Day 29) 2.68 0.00 4.88 0 33 Update Count (Day 1) 0.19 0.00 0.45 0 2 Update Count (Day 5) 1.07 0.00 1.69 0 8 Update Count (Day 10) 1.18 0.50 1.67 0 9 Update Count (Day 20) 1.74 1.00 2.41 0 14 Update Count (Day 25) 2.08 1.00 2.92 0 18 Update Count (Day 29) 2.71 1.00 3.64 0 23 Comment Count (Day 1) 3.65 0.00 19.66 0 246 Comment Count (Day 5) 9.54 0.00 53.54 0 680 Comment Count (Day 10) 11.79 0.00 68.57 0 871 Comment Count (Day 20) 16.75 0.00 94.39 0 1161 Comment Count (Day 25) 18.90 0.00 105.16 0 1251 Comment Count (Day 29) 22.81 0.00 124.84 0 1431 Projects Created 2.94 1.00 4.03 1 31 Projects Backed 11.69 1.00 33.74 0 245 Table 1 presents the descriptive statistics for the crowdfunding campaigns across different time points (Days 1, 5, 10, 20, 25, and 29). The number of funders increased over time, starting from a mean of 48.74 on Day 1 to 216.02 on Day 29, indicating substantial campaign growth. FAQ counts remained relatively low throughout the campaign, with mean values around 2–3. Update counts showed a gradual increase from 0.19 on Day 1 to 2.71 on Day 29, reflecting incremental campaign activity and communication with backers. Comment counts exhibited high variability, with means rising from 3.65 on Day 1 to 22.81 on Day 29 and standard deviations indicating occasional campaigns with very high engagement. The crowdfunding data show that funder activity grows sharply over time, with mean funders increasing from 48.74 on Day 1 to 216.02 on Day 29. This pattern reflects the early-stage dominance of information cascades, where initial backers make contributions based on observable signals rather than peer behavior, as indicated by relatively low median funder counts and minimal FAQ, update, and comment activity. In later stages, herding behavior becomes evident: rising mean funders, comment counts, and update activity suggest that subsequent backers are influenced by the accumulated social proof of campaign popularity, amplifying participation as the campaign progresses. 4.2 Main analysis of herding Table 2 Summary of results herding dynamics in reward based crowdfunding-using Odds Ratio (OR) Campaign Day Funders Growth Category Independent Variable (Lag) Odds Ratio R² χ² Updates (OR) Comments (OR) FAQs (OR) Projects Backed (OR) Projects Created (OR) Day 5 > 100 - 80 < 100 - 60 < 80 - 40 < 60 - 100 - 80 < 100 - 60 < 80 - 40 < 60 - 100 - 80 < 100 - 60 < 80 - 40 < 60 - 100 - 80 < 100 - 60 < 80 - 40 < 60 - 100 - 80 < 100 - 60 < 80 - 40 < 60 - <40 Day 25 1.62 1.1084 1.2074 0.00000173* 0.9940 0.9243 $- R 2 and χ² value for number of updates, number of comments and number of FAQs as independent variable and funders growth category as dependent variable. # - R 2 and χ² value for Funders as independent variable( Day 1,5,10,20 and 25) and funders growth category as dependent variable. *p value less than 0.05 (Detailed results in Appendix-I) Table 2 reports the results of our different panel regression on the presence of herding dynamics in reward-based crowdfunding based on the number of funders. This model refers to the dependent variables with number of funders segregated at different levels. Furthermore, following the Bouri et al. ( 2019 ) we replicate models using logistic regression. The reference level is less than 40 funders. The higher the odds ratio, the more difference with lower level of funders exists and more herding takes place. In order to understand the role of information cascades and herding, we carry our Logistic regression with comments from the backers, FAQs of the campaign by the creators, updates by the creators as independent variables at preceding days and total number of funders on a particular day. The day wise campaign results may presented as early days of the campaign i.e.5–10 days, middle of the campaign i.e. 20 days, acceleration phase of the campaign i.e. 25 days and the final closing stage i.e. 29 days. In the early stages, funder growth is strongly influenced by initial momentum, with odds ratios (OR = 1.02–1.03) indicating that early contributions increase the likelihood of higher subsequent funder growth. Interactive signals such as updates and comments exhibit very high odds ratios (OR = 1.80–7.70), demonstrating their significant role in boosting funder engagement. Prior creator experience, reflected in projects backed (OR = 1.02–1.16) and projects created (OR = 1.01–1.16), also positively affects funder attraction, suggesting that reputational capital enhances perceived trustworthiness. Odds ratios above unity across growth categories indicate that strong early participation substantially increases the probability of reaching higher funder levels. Updates, in particular, provide new information and reinforce trust, reducing uncertainty, while comments and FAQs further sustain momentum. The models’ explanatory power (R² = 0.19–0.40) confirms that early-stage engagement is a critical determinant of campaign outcomes, consistent with prior research highlighting the influence of cascades during the initial phases (Vismara, 2018 ; Xiao et al., 2021 ; Garnica et al., 2024). During the mid-campaign stage (Day 20), funder counts show a flat effect (OR ≈ 1.00, non-significant), while updates (OR ≈ 1.78–2.23) and comments (OR ≈ 1.84–2.66) emerge as the primary drivers of growth. At this stage, backers rely more on creator-provided information rather than sheer funder numbers, with FAQs playing a negligible role. Creator experience, captured by projects created (OR = 1.15) and backed (OR = 1.02), remains significant, reinforcing trust and mitigating information asymmetry. This pattern indicates that information cascades intensify, as new backers imitate earlier participants, laying the foundation for later-stage herding. In the acceleration phase (Day 25), funder counts demonstrate explosive growth (OR = 17.0–31.5), signaling that campaigns achieving critical mass attract disproportionate attention. Updates and comments remain significant (OR ≈ 1.57–1.71) but their relative influence declines compared to the dominant effect of a large funder base. Herding peaks here, with rapid amplification driven by social proof. By the campaign closing phase (Day 29), funder ORs remain strong (≈ 7.5–9.2) but growth begins to slow, reflecting saturation. Updates continue to play a key role in sustaining momentum (OR ≈ 2.0), while comments show mixed effects and FAQs become largely irrelevant. Creator experience maintains consistent though moderate effects (OR = 1.13–1.17), ensuring trust in campaigns led by experienced creators. Overall, these results illustrate a temporal shift in drivers of funder growth. Early-stage contributions and quality signals (updates, comments, creator experience) drive information cascades, while later stages are dominated by herding effects, where campaigns with large funder bases attract further support. This pattern aligns with prior literature highlighting the stage-wise dynamics of crowdfunding, where information cascades precede herding behavior (Vismara, 2018 ; Garnica et al., 2024; Kapounek & Kucerova, 2019; Comeig et al., 2020 ). 4.3 Further analysis of herding In the first step regarding analysis of herding, we used the theoretical framework developed in various literatures where information cascades takes place at the early stages and herding at the later stages. We have used several proxies to determine how the information cascades are developed in early stages. However, in order to confirm the fact that information cascades occur at the early stages and herding later, we use the Machine Learning methods. From the literatures, it is evident that there are various models for predictions of information cascades, which includes autoregressive models, decision tree, logistic regression, random forests and support vector mechanisms (Zhou et al., 2021). The use of ML methods have been a robustness test in crowdfunding (Yang et al., 2025 ; Elitzur and Solodoha, 2021 ) but mostly related to equity crowdfunding. The use of Linear Discriminant Analysis (LDA) for prediction of factors contributing success in RBCF (Oduro et al., 2022 ) have been demonstrated and the LDA approach is having more accuracy except the Linear regression models. Based on the literatures we use the LDA model to find out the role of updates and comments as well as projects created and backed by the creator. The results are presented in the tables. Table 3 Summary of results of LDA using funders as dependent variables and other variables (Project specific) Day(Dependent variables) Funders category Prior Probability Day(Independent variable) Funders FAQ_Count Update_Count Comment_Count LD1 (%) LD1 Centroid LD2 Centroid Day5 > 100 0.2500 Day1 166.682 4.795 5.227 83.932 91.3 −1.3574 0.104 > 80 60 40 < 60 0.0852 20.733 2.600 4.067 4.000 0.0579 −0.626 100 0.2330 Day5 390.976 3.976 2.073 37.756 88.9 − − > 80 60 40 < 60 0.1080 43.158 1.632 1.737 1.632 − − 100 0.2955 Day10 365.269 4.173 2.115 38.269 83.3 −0.984 0.130 > 80 60 40 < 60 0.0909 38.875 2.000 1.500 1.563 0.123 −0.434 100 0.3239 Day20 444.211 3.842 3.053 49.474 84.9 −0.816 0.102 > 80 60 40 < 60 0.0852 44.200 2.333 1.933 2.267 0.113 −0.310 100 0.3523 Day25 465.984 4.032 3.532 51.694 78.4 −0.686 0.285 > 80 60 40 < 60 0.0795 41.357 2.429 1.643 1.357 0.230 −0.158 < 40 0.4716 9.253 1.819 0.675 0.422 0.598 −0.040 Table 4 Summary of results of LDA using funders as dependent variables and other variables (creator specific) Day(Dependent variables) Funders Category Prior Probability Projects Created (Mean) Projects Backed (Mean) LD1 Loading: Projects Created LD1 Loading: Projects Backed Group Centroid LD1 Group Centroid LD2 Day 5 > 100 0.2500 4.818 21.795 0.4322 0.0277 1.522 0.116 Day 5 > 80 60 40 < 60 0.0852 3.733 12.467 -0.7179 -0.0901 -0.010 -0.607 Day 5 100 0.2330 4.854 22.537 0.2700 0.0196 1.414 0.083 Day 10 > 80 60 40 < 60 0.1080 3.579 18.684 -0.7083 0.000265 -0.093 -0.616 Day 10 100 0.2955 4.462 22.788 0.2561 0.0266 1.070 -0.141 Day 20 > 80 60 40 < 60 0.0909 3.063 21.063 -0.9716 0.0951 -0.043 0.420 Day 20 100 0.3523 4.175 20.789 0.2862 0.0241 0.874 0.112 Day 25 > 80 60 40 < 60 0.0852 2.533 13.800 0.874 -0.0653 -0.114 -0.230 Day 25 100 0.3239 4.000 19.403 0.2263 0.0165 0.742 0.273 Day 29 > 80 60 40 < 60 0.0852 1.714 3.143 0.0793 0.0392 -0.340 -0.183 Day 29 < 40 0.4716 2.048 4.205 - - -0.647 -0.033 (Detailed results in Appendix-II ) Linear Discriminant Analysis across Days 5, 10, 20, 25, and 29 confirms that early and sustained engagement signals—particularly updates, comments, and creator experience—strongly predict funder growth. On Day 5, most campaigns ( 100 funders) displayed markedly higher activity (~ 167 funders, ~ 5 updates, ~ 84 comments). LD1 accounted for 91.3% of discrimination, indicating that early signals effectively distinguish highly successful campaigns from low-performing ones. By Day 10, high-success campaigns (> 100 funders) increased to 23.3%, anchored by active commenting and updates (~ 391 funders, ~ 2 updates, ~ 38 comments), with LD1 explaining > 83% of discrimination. Day 20 LDA confirmed that updates and comments at Day 10 remain strong predictors, with creator experience reinforcing credibility and encouraging herding. In the acceleration phase (Day 25), LD1 captured ~ 85% of discrimination, showing sustained momentum and strong herding effects among campaigns achieving > 100 funders. At campaign closure (Day 29), Day 25 funders continued to predict success (LD1 ≈ 78%), with high-performing campaigns maintaining active engagement, while mid-tier and low-performing campaigns remained less distinct. Overall, these results indicate a temporal shift in drivers of funder growth. Early-stage contributions and quality signals initiate information cascades, which evolve into herding behavior as campaigns progress. High-performing campaigns consistently exhibit strong engagement across updates, comments, and creator experience, whereas mid-tier campaigns with weaker early signals show overlapping centroids and less predictable outcomes. These findings underscore the critical role of early and sustained engagement in driving reward-based crowdfunding success. 5. Discussions The implications of the findings have been summarized as follows. 5.1. Theoretical Implications This paper contributes to the literature on reward-based crowdfunding by highlighting how signaling and herding dynamics influence the success of campaigns. The results indicate that early backers tend to make informed decisions based on publicly available quality signals, drawing on their abilities and expertise to evaluate new projects. The findings support the two-stage model of collective decision-making in crowdfunding: a) Information cascades dominate in the early stages , where backers imitate prior funders based on visible quality signals (updates, comments, creator’s experience early contributions). b) Herding behavior emerges in the later stages , once campaigns achieve momentum, where funder counts themselves drive exponential growth. This duality aligns with Kapounek & Kucerova (2019), Comeig et al. ( 2020 ), and Garnica et al. (2024), who suggest cascades transform into herding as campaigns mature. The sharp increase in odds ratios for funder counts during the acceleration phase(later phases) indicates a threshold have been reached .Herding is not linear—it becomes dominant only after campaigns accumulate a sufficiently large base of early backers. This reinforces theories of non-linear dynamics in collective funding, where popularity cues trigger disproportionately large behavioral responses. Updates, comments, and FAQs play a catalytic role in shaping cascades that later transform into herding. The results imply that herding is not a purely irrational “blind following” phenomenon, but is anchored in quality signals that reduce uncertainty in early stages. Even during strong herding phases; updates and comments remain significant—though secondary. This demonstrates that herding does not substitute quality signals , but rather interacts with them to reinforce growth . Theoretically, this extends signaling theory by showing how dynamic interactions between quality signals and social proof sustain momentum. Herding is not uniformly beneficial. The creator’s experience in form of projects backed and projects created are important particularly during the early stages of the campaign, which also acts as quality signals during funding by early backers. This highlights that herding requires consistent reinforcement , not just early momentum. 5.2. Implications for entrepreneurs and policy makers For Entrepreneurs (Campaign Creators) Since early funder counts drive information cascades, creators must focus on securing strong initial backer support (friends, family, early investors) within the first 5–10 days. Pre-launch marketing, warm leads, and exclusive early-bird rewards help trigger cascades. Updates, comments, and FAQs act as trust-building mechanisms. Posting frequent, transparent updates and encourage active commenting from early backers. FAQs should be used mainly in the early and middle stages, but are less useful toward the end. By Day 20, funder count alone is no longer a predictor of growth—backers look for creator responsiveness and community activity. Once critical mass is achieved, campaigns can experience exponential herding effects. Time strategic updates (stretch goals, bonus rewards, partnerships) to maximize late-stage inflows. In closing stages, updates are critical to maintain momentum and trigger last-minute contributions. For policy makers Since cascades rely on visible signals, platforms should ensure transparent and accessible campaign data (funding progress, updates, backer interactions). Policymakers can encourage platforms to design mechanisms (e.g., bonus rewards, matching grants) that reward early contributions, as these have disproportionate influence. Herding can lead to overfunding of popular but low-quality projects, creating systemic risks. Policymakers may consider disclosure requirements (project progress, creator history, financial details) to ensure herding is supported by quality signals. To mitigate risks of misinformed herding, rules could require platforms to flag campaign credibility indicators (verification badges, creator reputation scores). 6. Limitations and Scope for Future Research This study considered primarily numerical variables, leaving scope for the inclusion of categorical variables related to the creator—such as age, gender, and professional experience (Rossi et al., 2021 )—in future research. Another promising avenue is to examine manipulative practices that may distort funding patterns (Meoli & Vismara, 2021). For instance, early backers may initially invest heavily to signal project quality, only to withdraw later after attracting a large pool of late backers. Similarly, creators may engage in impression management (Gleasure, 2015), which could amplify herding effects. 7. Conclusions This study examines the temporal dynamics of reward-based crowdfunding and shows how information cascades and herding evolve across campaign stages. In the early phase, strong initial participation and visible quality signals—particularly updates and comments along with creator’s experience in form of projects backed —create information cascades that shape perceptions of project quality and encourage further contributions. By the midpoint, the marginal impact of funder counts diminishes, while sustained engagement through creator updates and backer comments becomes the primary determinant of growth. In the acceleration phase, once campaigns achieve threshold, herding effects dominate, with funder base size driving exponential growth while updates and comments provide supportive but secondary reinforcement. In the closing stage, growth slows naturally, yet timely updates and urgency cues remain critical to sustaining momentum, whereas FAQs lose relevance and comments show mixed influence. The results highlight a dual-phase process: cascades in the early stages and herding in later stages, consistent with theories of social proof and collective decision-making. Practically, campaign creators should prioritize early momentum, continuous engagement, and effective end-stage communication, while policymakers and platforms should enhance transparency, promote interaction, and safeguard against herd-driven misallocations. Overall, crowdfunding success emerges from the interplay of early signals, engagement, and popularity, offering valuable insights for both theory and practice. Declarations This research adheres to all ethical and confidentiality requirement. Funding: This research has not received any external funding. Conflicts of Interest/Competing Interests : The authors declare no conflict of interest. Availability of data and materials: The datasets used and/or analyzed in this study are available from the corresponding author on reasonable request. Author Contributions: All authors have equally participated in the research. All authors have read and agreed to the final version of the manuscript. Acknowledgement: The authors are sincerely thankful to all anonymous referees whose valuable comments help to improve the quality of this paper. Human Ethics and Consent to Participate declarations : ‘Not applicable’ References Ahlers GKC, Cumming D, Günther C, Schweizer D (2015) Signaling in equity crowdfunding. Entrepreneurship Theory Pract 39(4):955–980. https://doi.org/10.1111/etap.12157 Akerlof GA (1970) The market for lemons: Quality uncertainty and the market mechanism. Q J Econ 84(3):488–500 Aleksina N, Akulenka S, Lublóy Á (2019) Success factors of crowdfunding campaigns in medical research: Perceptions and reality. Drug Discovery Today 24(7):1413–1420. https://doi.org/10.1016/j.drudis.2019.05.012 Amirat A, Alwafi W (2020) Does herding behavior exist in cryptocurrency market? Cogent Econ Finance 8(1). https://doi.org/10.1080/23322039.2020.1735680 Anglin AH, Short JC, Drover W, Stevenson RM, McKenny AF, Allison TH (2018) The power of positivity? The influence of positive psychological capital language on crowdfunding performance. J Bus Ventur 33(4):470–492. https://doi.org/10.1016/j.jbusvent.2018.03.003 Åstebro T, Fernández M, Lovo S, Vulkan N (2024) Herding in equity crowdfunding. RAND J Econ 55(3):403–441 Astebro TB, Fernández, Sierra (2019) Manuel and Lovo, Stefano and Vulkan, Nir, Herding in Equity Crowdfunding18, (June Paris December 2018 Finance Meeting EUROFIDAI - AFFI, RAND Journal of Economics, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3084140 or http://dx.doi.org/10.2139/ssrn.3084140 Belleflamme P, Lambert T, Schwienbacher A (2014) Crowdfunding: Tapping the right crowd. J Bus Ventur 29(5):585–609. https://doi.org/10.1016/j.jbusvent.2013.07.003 Blohm I, Antretter T, Sirén C, Grichnik D, Wincent J (2022) It’s a people’s game, isn’t it?! A comparison between the investment returns of business angels and machine learning algorithms. Entrepreneurship Theory Pract 46(4):1054–1091. https://doi.org/10.1177/1042258720945206 Bouri E, Gupta R, Roubaud D (2019) Herding behaviour in cryptocurrencies. Finance Res Lett 29:216–221. https://doi.org/10.1016/j.frl.2018.07.008 Butticè V, Di Pietro F, Tenca F (2020) Is equity crowdfunding always good? Deal structure and the attraction of venture capital investors. J Corp Finance 65:101773. https://doi.org/10.1016/j.jcorpfin.2020.101773 Chan CSR, Parhankangas A, Sahaym A, Oo P (2020) Bellwether and the herd? Unpacking the U-shaped relationship between prior funding and subsequent contributions in reward-based crowdfunding. J Bus Ventur 35(2). Article 105934. https://doi.org/10.1016/j.jbusvent.2019.04.002 Cillo V, Petruzzelli AM, Ardito L, Del Giudice M (2019) Understanding sustainable innovation: A systematic literature review. Corp Soc Responsib Environ Manag 26(5):1012–1025 Colombo MG, Franzoni C, Rossi-Lamastra C (2015) Internal social capital and the attraction of early contributions in crowdfunding. Entrepreneurship Theory Pract 39(1):75–102 Comeig I, Mesa-Vázquez E, Sendra-Pons P, Urbano A (2020) Rational herding in reward-based crowdfunding: An MTurk experiment. Sustainability 12(23):9827 Cordova A, Dolci J, Gianfrate G (2015) The determinants of crowdfunding success: Evidence from technology projects. Procedia – Social Behav Sci 181:115–124. https://doi.org/10.1016/j.sbspro.2015.04.872 Courtney C, Dutta S, Li Y (2017) Resolving information asymmetry: Signaling, endorsement, and crowdfunding success. Entrepreneurship Theory Pract 41(2):265–290. https://doi.org/10.1111/etap.12267 Crosetto P, Regner T (2014) Crowdfunding: Determinants of success and funding dynamics. Jena Economic Res Papers, 2014–2035 Dai H, Zhang DJ (2019) Prosocial goal pursuit in crowdfunding: Evidence from Kickstarter. J Mark Res 56(3):498–517. https://doi.org/10.1177/0022243718821697 Dao D, Nguyen T, Andrikopoulos P (2024) Herding dynamics and multidimensional uncertainty in equity crowdfunding: The impacts of information sources. Inf Manag 61(1):103889. https://doi.org/10.1016/j.im.2023.103889 Deng L, Ye Q, Xu D, Sun W, Jiang G (2022) A literature review and integrated framework for the determinants of crowdfunding success. Financial Innov 8(1):1–70 Dhochak M, Pahal S, Doliya P (2022) Predicting startup valuation: A deep learning approach. Venture Capital 26(1):75–99. https://doi.org/10.1080/13691066.2022.2161968 Elitzur R, Katz N, Muttath P, Soberman D (2024) The power of machine learning methods to predict crowdfunding success: Accounting for complex relationships efficiently. J Bus Venturing Des 3:100022. https://doi.org/10.1016/j.jbvd.2024.100022 Elitzur R, Solodoha E (2021) Does gender matter? Evidence from crowdfunding. J Bus Venturing Insights 16:e00268. https://doi.org/10.1016/j.jbvi.2021.e00268 Ferrati F, Muffatto M (2021) Reviewing equity investors’ funding criteria: A comprehensive classification and research agenda. Venture Capital 23(2):157–178. https://doi.org/10.1080/13691066.2021.1883211 Hadačová D, Maličká L (2022) Reward-Based Crowdfunding. How to Make it Work? Theory, Methodology . Pract – Rev Bus Manage 18(01):45–59. https://doi.org/10.18096/TMP.2022.01.04 Hagawe HM, Mobarek A, Hanuk A, Jamal A (2023) A unique business model for microfinance institution: the case of Assadaqaat Community Finance (ACF). https://doi.org/10.1080/23311975.2022.2135202 . Cogent Bus Manag 10 Hornuf L, Schwienbacher A (2018) Market mechanisms and funding dynamics in equity crowdfunding. J Corp Finance 50:556–574. https://doi.org/10.1016/j.jcorpfin.2017.08.009 Huang Y, Uy MA, Liu C, Foo MD, Li ZA (2023) Visual totality of rewards-based crowdfunding pitch videos: Disentangling the impact of peak negative affective visual expression on funding outcomes. J Bus Ventur 38:106318. https://doi.org/10.1016/j.jbusvent.2023.106318 Huang S, Pickernell D, Battisti M et al (2022) Signalling entrepreneurs’ credibility and project quality for crowdfunding success: cases from the Kickstarter and Indiegogo environments. Small Bus Econ 58:1801–1821. https://doi.org/10.1007/s11187-021-00477-6 Jiang Y, Ho YC, Yan X, Tan Y (2018) Investor platform choice: Herding, platform attributes, and regulations. J Manage Inform Syst 35(1):86–116 Jiang Y, Ho YC, Yan X, Tan Y (2022) What’s in a username? The effect of perceived anonymity on herding in crowdfunding. Inform Syst Res 33(1):1–17 Kaartemo V (2017) The elements of a successful crowdfunding campaign: A systematic literature review of crowdfunding performance. Int Rev Entrepreneurship 15:291–318 Kapounek S, Kucerová Z (2019) Overfunding and signaling effects of herding behavior in crowdfunding (CESifo Working Paper No. 7973). CESifo Litjens G, Sánchez C, Timofeeva N et al (2016) Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep 6:26286. https://doi.org/10.1038/srep26286 Mollick, Ethan The dynamics of crowdfunding: An exploratory study,Journal of Business Venturing, 29, Issue 1,2014,Pages 1–16,ISSN 0883–9026, https://doi.org/10.1016/j.jbusvent.2013.06.005 Moritz A, Block JH (2016) Crowdfunding: A Literature Review and Research Directions. In: Brüntje D, Gajda O (eds) Crowdfunding in Europe. FGF Studies in Small Business and Entrepreneurship. Springer, Cham. https://doi.org/10.1007/978-3-319-18017-5_3 Oduro MS, Yu H, Huang H (2022) Predicting the entrepreneurial success of crowdfunding campaigns using model-based machine learning methods. Int J Crowd Sci 6(1):7–16. https://doi.org/10.26599/IJCS.2022.9100003 Petit A, Wirtz P (2022) Experts in the crowd and their influence on herding in reward-based crowdfunding of cultural projects. Small Bus Econ 58:419–449. https://doi.org/10.1007/s11187-020-00424-x Posch L, Mladenow A, Strauss C (2022) Reward-based Crowdfunding: successful signaling from an entrepreneur. CEUR Workshop Proceedings , 3171 , 999–1014 Ralcheva A, Roosenboom P Forecasting Success in Equity Crowdfunding (January 2019). Available at SSRN: https://ssrn.com/abstract=3260140 or http://dx.doi.org/10.2139/ssrn.3260140 Rodríguez-Garnica G, Gutiérrez-Urtiaga M, Tribo JA (2025) Signaling and herding in reward-based crowdfunding. Small Bus Econ 64:889–916. https://doi.org/10.1007/s11187-024-00933-z Rossi A, Vanacker T, Vismara S (2021) Equity crowdfunding: New evidence from US and UK markets. Rev Corp Finance 1:407–453. https://doi.org/10.1561/114.00000009 Scharfstein DS, Stein JC (1990) Herd behavior and investment. Am Econ Rev, 465–479 Shneor R, Vik AA (2020) Crowdfunding success: a systematic literature review 2010–2017. Baltic J Manage 15(2):149–182 Spyrou S (2013) Herding in financial markets: a review of the literature. Rev Behav Finance 5(2):175–194 Usman SM, Bukhari FAS, You H, Badulescu D, Gavrilut D (2020) The Effect and Impact of Signals on Investing Decisions in Reward-Based Crowdfunding: A Comparative Study of China and the United Kingdom. J Risk Financial Manage 13(12):325. https://doi.org/10.3390/jrfm13120325 Vismara S (2018) Information Cascades among Investors in Equity Crowdfunding. Entrepreneurship Theory Pract 42(3):467–497. https://doi.org/10.1111/etap.12261 Vismara S (2016) Equity retention and social network theory in equity crowdfunding. Small Bus Econ 46:579–590. https://doi.org/10.1007/s11187-016-9710-4 Wang W, He L, Wu YJ, Goh M (2021) Signaling persuasion in crowdfunding entrepreneurial narratives: The subjectivity vs objectivity debate. Comput Hum Behav 114:106576. https://doi.org/10.1016/j.chb.2020.106576 Wohlgemuth,Veit Elisabeth SC, Berger M, Wenzel More than just financial performance: Trusting investors in social trading. J Bus Res, 69, Issue 11,2016,Pages 4970–4974,ISSN 0148–2963, https://doi.org/10.1016/j.jbusres.2016.04.061 Xiao S, Ho YC, Che H (2021) Building the momentum: Information disclosure and herding in online crowdfunding. Prod Oper Manage 30(9):3213–3230 Yang J, Xin J, Zeng Y et al (2025) Signaling and perceiving on equity crowdfunding decisions — a machine learning approach. Small Bus Econ 65:315–356. https://doi.org/10.1007/s11187-024-00991-3 Zhang J, Liu P (2012) Rational herding in microloan markets. Manage Sci 58(5):892–912. https://doi.org/10.1287/mnsc.1110.1459 Zhou F, Xu X, Trajcevski G, Zhang K (2022) A survey of information cascade analysis: Models, predictions, and recent advances. ACM-CSUR 54(2). Article 27. https://doi.org/10.1145/3433000 Zhu Z, Liu J, Zhang M (2022) Rethinking investors' herding behavior under the conditions of reward-based crowdfunding platform. Industrial Manage Data Syst 122(12):2762–2782 Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8601520","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596875857,"identity":"e8ecfd05-0e5e-4cc4-9718-4d0fa605f861","order_by":0,"name":"Subhash Karmakar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYFACHjYgcSCBgZn5AAMDmwQ/ewOQ+4A4LWwJIC2SPQfAXGK0MPAYALUwgLUw4NMiPyP32IMff+7k8bPzfPvwocxCgkfs8EOgCXZyug3YtRjcyEs37G17VizZzLt55oxzEhI80mkGQC3JxmYHcGiRyDGT4G04nLjhMO9mZt42iTp76QSQlgOJ23BokZ+RYyb55w9IC89j5r9tIFvSP+DVwnAjx0yahw2shZmZEawlB78tBmfepUnLth0G+oXNmLEH7JecggMJBrj9It+ee0zyzZ/Defz8hx8z/CirAzls84cPFXZyuLTgAgakKR8Fo2AUjIJRgAoAvT1fZ4BHVlkAAAAASUVORK5CYII=","orcid":"","institution":"Bank Of India Staff Training College","correspondingAuthor":true,"prefix":"","firstName":"Subhash","middleName":"","lastName":"Karmakar","suffix":""},{"id":596875858,"identity":"ac60cda0-da35-44e3-bd3a-2babb40e816a","order_by":1,"name":"Gautam Bandyopadhyay","email":"","orcid":"","institution":"National Institute of Technology Durgapur","correspondingAuthor":false,"prefix":"","firstName":"Gautam","middleName":"","lastName":"Bandyopadhyay","suffix":""},{"id":596875859,"identity":"3168bbc8-fc1d-4a49-aee7-1638c8b1266e","order_by":2,"name":"Jayanta Nath Mukhopadhyay","email":"","orcid":"","institution":"Helios Mutual Funds","correspondingAuthor":false,"prefix":"","firstName":"Jayanta","middleName":"Nath","lastName":"Mukhopadhyay","suffix":""}],"badges":[],"createdAt":"2026-01-14 12:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8601520/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8601520/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103595292,"identity":"9d25f7ae-e33e-478f-895d-54c0ed0de1ed","added_by":"auto","created_at":"2026-02-27 12:56:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47785,"visible":true,"origin":"","legend":"\u003cp\u003eHerding phenomena in reward based crowdfunding\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8601520/v1/062b81f14f04f7680a6ca90d.png"},{"id":103595270,"identity":"ce1a680c-5def-4bfd-a7b6-ec37a04c7121","added_by":"auto","created_at":"2026-02-27 12:56:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45196,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInterrelationship with hypothesis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8601520/v1/e967025dd750d825fc71bc69.png"},{"id":104399018,"identity":"7fdaa71d-a507-4807-b110-c3b6d514d08f","added_by":"auto","created_at":"2026-03-11 12:04:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2137990,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8601520/v1/50fae18e-0de1-4c2e-8038-5c9f1fafa7e5.pdf"},{"id":103595269,"identity":"e3cfbcef-e1c6-4f6a-8e32-157f462306cc","added_by":"auto","created_at":"2026-02-27 12:56:21","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":334921,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8601520/v1/1311288d63bd7370aedc32da.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Crowdfunder’s Paradox and Herding dynamics in Reward Based Crowdfunding","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eA general tendency in today\u0026rsquo;s crowdfunding ecosystem is to identify factors that contribute positively towards success of the Crowd Funding (CF) campaigns. Literatures in this area contributes towards the growth of the crowdfunding ecosystem and promote innovation, where focus on sustainable innovation has opened a new vista of research.\u003c/p\u003e \u003cp\u003eEntrepreneurs remain engaged in seeking new and innovative ways to attract investors or funders to fund their projects. The growth and proliferation of crowdfunding since 2008 have helped the entrepreneurs to collect small amount of funds without any hindrance, where there has been a shift from the traditional and sustainable channels of finance (Cillo et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Given that this may bear a lot of constraints and obstacles, entrepreneurs are now looking forward towards Crowd Funding (CF). Therefore, CF has been a unique way of entrepreneurial finance, which is has its own nature and can be distinguished from other forms of financing Viz. angel investors, Venture capital etc. (Rossi et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). CF works on wide geographical areas via online platforms which supports the campaigns rolled out by the entrepreneurs, the \u003cb\u003e\u0026ldquo;campaigner\u0026rdquo; or the \u0026ldquo;creator\u0026rdquo;\u003c/b\u003e of the project who generally comes out with an idea and the \u003cb\u003e\u0026ldquo;backers \u0026ldquo; or \u0026ldquo;funders\u0026rdquo;\u003c/b\u003e who ultimately contribute funds for the completion of the project.\u003c/p\u003e \u003cp\u003eCF typically refers to a paradigm where merging of funds from various small investors that circumvents the normal funding sources and thereby meet the expectation of entrepreneurs and in the process crowd funders also enjoys the \u0026ldquo;community benefits\u0026rdquo;(Belleflamme et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Moritz and Block ,2016).\u003c/p\u003e \u003cp\u003eFor our purpose, we have focused on Reward-Based Crowd Funding (RBCF) which has gained considerable importance for financing start-ups and novel concepts. In \u003cb\u003eRBCF\u003c/b\u003e, the creator of a project introduces an idea or product on an online platform to attract backers who can help finance its development. The risk is inherently high because creators often have little initial knowledge of market acceptance, and backers lack certainty regarding the project\u0026rsquo;s eventual success. This model may also involve \u003cem\u003epreselling\u003c/em\u003e products during early stages (Vismara, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe distinct features of the RBCF provides ample scope and opportunities to distinctly study the role of information signals as well as their role in form of herding and information cascades at various stages of the campaign. Literature has shown the effectiveness of signals in defining the success of RBCF campaigns are campaign features\u0026mdash;such as updates and comments that have shown a positive association with funding outcomes (Shenor \u0026amp; Vik, 2020; Hadacova \u0026amp; Malicka, 2022,Pocsh et.al.,2022). The role of funders are also important and a campaign, which can attract more number of funders, have a better likelihood of success as compared to others. In this context, it is very important to identify the role of herding and information cascades. Literatures have studied that signals play a critical role in influencing backer behavior. Studies have examined how signals\u0026mdash;such as funding goals, timely updates, and clear descriptions, interact with herding behavior (Garnica et al., 2024; Shneor \u0026amp; Vik, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Comeig et al.,2020). These signals not only attract early backers but also help build credibility, influence subsequent contributions. \u003cem\u003eIn this paper, we have identified multiple quality signals such as number of comments by backers, number of updates by creators that requires regular monitoring to enhance the success predictability including how they are influencing the growth of funders. In addition to that, we have included the skill and experience of the entrepreneur in form of projects created and projects backed by creators also. Our paper differentiates from the rest of the literatures since we have paid attention to changes in the number of funders at specific intervals over the campaign period enforcing the herding dynamics.\u003c/em\u003e The previous studies have provided a wide range of evidences on herding behavior in crowdfunding, but it is not still clear that how herding occurs in RBCF particularly the nature and stage of campaigns. \u003cem\u003eWe attempt to fill this gap by investigating the dynamics of crowdfunding stage wise based on live campaigns\u003c/em\u003e. \u003cem\u003eIn addition to that, we have also utilized the projects created and backed by the creator related to their experience to reveal how they are influencing herding at the later or earlier stages of the campaign and attracting more funders in form of information cascades and herding. Another aspect discussed in the paper is related to use of Machine Learning (ML) methods in prediction of herding dynamics\u003c/em\u003e. The predictability of success in crowdfunding have been mostly inferred through traditional regression methods but recently ML methods such as Linear Discriminant Analysis(LDA) (Oduro et.al.,2022), boosted trees and neural networks have been used ( Elitzur et al.,2024). We have used ML techniques in form of LDA to predict the role of information cascades and herding at various stages of the campaign.\u003c/p\u003e \u003cp\u003eIn sum, our contributions towards crowdfunding literature has been three folds. First, we examine the different information signals with their distinct and specific roles at various stages of the campaign, which shall help in understanding the funding dynamics with reference to herding at various stages of the campaign. Second, we also infer to the role regarding experience and skills of the creator in form of projects backed and created to define relationship with attraction of funders. Thirdly, the use of ML methods to improve the prediction of herding.\u003c/p\u003e"},{"header":"2. Theoretical Framework and Hypothesis development","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Theoretical background and Literature review\u003c/h2\u003e \u003cp\u003eCrowdfunding (CF) manifests in different forms depending on the expected rewards or returns for backers or investors. Reward-based crowdfunding (RBCF) typically involves project creators presenting new ideas or products on online platforms to attract funding for development, often through early-stage pre-sales (Vismara, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Backers may be motivated by monetary returns, personal support, or intrinsic satisfaction (Hagawe et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). RBCF thus provides an important context to study behavioral phenomena such as herding, as well as the dynamics of success factors. Campaign success is most commonly measured by whether the funding goal is achieved (Colombo et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Buttice et al., 2017; Anglin et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Deng et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).Research on crowdfunding success has examined both campaign-specific factors and the role of early backers. Studies by Mollick (2014), Cordova (2015), Crosetto and Regner (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and Vismara (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) emphasize campaign attributes, while Astebro et al. (2024) and Dao et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) highlight the creator\u0026rsquo;s influence. Specific determinants of RBCF success include project goals, funding targets, backer numbers, and pledge amounts (Mollick, 2014); platform and campaign-related characteristics (Kaartemo, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); and creator updates and backer comments (Hornuf \u0026amp; Schwienbacher, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). All stakeholders\u0026mdash;including platforms, creators, and backers\u0026mdash;play a critical role in campaign outcomes (Dai \u0026amp; Zhang, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additional contributors include external funding, the experience and age of project directors (Rachelva \u0026amp; Roosenboom, 2020), and whether the creator is a serial crowdfunder (Usman et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Elitzur et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe dynamics of funding across campaign stages remain a key area of investigation. Herding behavior, where backers imitate the decisions of others, has been widely documented in crowdfunding (Astebro et al., 2018; Chan et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang \u0026amp; Liu, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Information cascades, where investors disregard private information in favor of early signals, are considered a subset of herding (Scharfstein \u0026amp; Stein, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Spyrou, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Aleksina et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kapounek \u0026amp; Kucerova, 2019) and often occur in early stages before being replaced by herding behavior as campaigns progress (Garnica et al., 2024).\u003c/p\u003e \u003cp\u003eInformation asymmetry between creators and backers is a fundamental challenge in crowdfunding. Backers may lack knowledge of the entrepreneur\u0026rsquo;s skills, product quality, or business operations (Huang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ahlers et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). To mitigate this, project creators can signal quality through visible cues, influencing potential backers\u0026rsquo; decisions (Belleflamme et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These signals can relate to both the project and the creator (Courtney et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and often build trust within the community, enabling others to follow early backers\u0026rsquo; actions (Akerlof, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1970\u003c/span\u003e; Wohlgemuth et al., 2016). Such signaling generates information cascades that contribute to herding behavior. This paper examines herding dynamics in RBCF by focusing on two key aspects. First, it investigates how visible project and creator-related signals influence herding at different stages of the campaign. Second, it explores the transition from early-stage information cascades to later-stage herding, where backers may make suboptimal decisions by relying on early contributors. Understanding these interactions offers insights into the behavioral and informational mechanisms driving crowdfunding success (Garnica et al., 2024; Shenor \u0026amp; Vik, 2020).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Use of Machine Learning in Crowdfunding arena\u003c/h2\u003e \u003cp\u003eIn crowdfunding research, machine learning (ML) methods have been increasingly applied to predict factors contributing to campaign success (Dhochak et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Blohm et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Ferrati, \u0026amp; Muffatto, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). ML approaches are particularly useful for distinguishing between subjective and objective features affecting success (Wang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and often outperform standard binary logit models due to their ability to capture complex relationships among predictors (Elitzur et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, we employ Linear Discriminant Analysis (LDA) using Jamovi software to predict the success of equity crowdfunding campaigns (Elitzur et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). ML methods have also been used as robustness checks in prior research (Elitzur \u0026amp; Solodoha, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Notably, LDA has not yet been applied to reward-based crowdfunding, and our study addresses this gap by examining herding-related factors at different stages of crowdfunding campaigns (Oduro et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the previously mentioned studies the dynamics of herding may be put as:\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Hypothesis development\u003c/h2\u003e \u003cp\u003eHence, following the theory proposed by Garnica et.al. (2024) and Vismara (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) where information cascades generated by the signals by early backers and experience of the creators which are later on replaced by herding of late backers, we argue that herding may be less intensive during the early stages of the campaign when the main constraints are availability of information. The late backers in case of crowdfunding takes suboptimal decisions based on the comments of early backers and updates by the creators.\u003c/p\u003e \u003cp\u003eWe therefore hypothesize that:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1: Herding occurs during the early stages of a campaign\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe second hypothesis is\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2: Herding occurs during the later stages of the campaign\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology and Data","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data\u003c/h2\u003e \u003cp\u003eThe sample contains 176 campaigns from the Kickstarter platform with 1056 observations over 29 days of the campaign starting from Day 1 to Day 29. Our sample size is similar to that of data sample used by Dao et al.(2024).We have collected the data during various periods of the campaign such as first day, fifth day, tenth day, twentieth day, twenty fifth day and twenty ninth day. The projects in our sample includes all the categories (Arts,crafts,comics,dance,design,fashion,film,food,games,journalism,music,photography,publishing,technology and theater).The data was captured in ongoing campaigns during March2025 and April 2025.We could obtain some basic features of the campaign such as number of funders, number of updates by creators, number of comments by backers, number of projects created and backed by the creator.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMeasuring the herding momentum\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn line with the prior literature available (Jiang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dao et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) the herding momentum in crowdfunding is based through sequential correlation of the number of funders. \u003cem\u003eUnder these circumstances, the information on total number of funders, which is available on the platform, shall attract more funders if herding exists (\u003c/em\u003eDao et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this approach the Log of daily investors at time t are examined with the Log of lag of daily investors at time t-1 and regression has been run.\u003c/p\u003e \u003cp\u003eLog DailyInvestors\u003csub\u003ejt =\u003c/sub\u003e \u003cb\u003eα\u003c/b\u003e\u003csub\u003e\u003cb\u003e0+\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eꞵLogLagInvestor\u003c/b\u003e\u003csub\u003e\u003cb\u003ejt-1+ƳXJT+\u003c/b\u003e\u003c/sub\u003e \u003cb\u003ee\u003c/b\u003e \u003csub\u003ejt\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e \u003csub\u003eWhere\u003c/sub\u003e j is the number of the listed projects, t\u0026thinsp;=\u0026thinsp;2,\u0026hellip;\u0026hellip;., T, and \u003csub\u003e\u003cb\u003eXJT\u003c/b\u003e\u003c/sub\u003e includes a number of campaign specific variables. In this case the funding period is divided into sub-periods in which the first covers from the first day to middle day of the campaign and the second set with rest of the days. Herding is specified in the first set of days if \u003cb\u003eꞵ is positive and significant in the first part and the converse is true if it is ꞵ is positive and significant in the second part.\u003c/b\u003e \u003cem\u003eHowever, this approach is based on the assumption that the number of previous investors will not decrease over time, which may not be applicable to reward based crowdfunding where any backer may pull out from the campaign at any time before the campaign closes.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eVarious literatures have approached the role of herding in RBCF where there is role of early backers opinion (Petit and Writz ,2022 ;\u003c/b\u003e Comeig et.al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kapounek and Kucerova, 2019) and the herding thereafter is observed at the later stages of the campaign. Another important phenomena observed in herding is related to the fact that prior funding of the campaigns with inverted U shaped effects and it is further strengthened by frequent updates(Zhu et al.,2022)\u003c/p\u003e \u003cp\u003eAccording to prior literatures (Garnica et al., 2024, Vismara, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) herding is observed at the later stages of the campaign and information cascade at the early stages (Garnica et al., 2024). Accordingly, to measure the backer\u0026rsquo;s behavior the time has been considered as an important factor such as time to reach 10% of funding goal over the total duration of the campaign and a low value indicates early funding (Garnica et al., 2024).\u003c/p\u003e \u003cp\u003eHerding increases the number of funders and accordingly we have number of funders as dependent variable (Jiang et al, 2024, Chan et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e and Dao et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsidering the fact that and constraints where in RBCF campaigns backers may pull out from the campaign, we adopt an approach based on herding in financial markets where Bouri et al.(2019) found that herding behavior can be explained by presence of uncertainty using Logistic Regression. Several authors in case of cryptocurrency have further extended this theory where Logistic regression have been used to confirm the results related to herding dynamics (Amirat and Alwafi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe use this approach to get a view how information signals play a particular role in herding and to understand the influence of various factors, which are contributing towards herding at various stages of the campaign.\u003c/p\u003e \u003cp\u003eTo understand the temporal dynamics of herding we follow this approach where log of the odds have been examined with dependent variable as the total number of funders at day t of a campaign j and independent variables as number of total funders at day t-p (lag days/previous days), where p refers to previous days. If log of odds is significant and more than 1, the independent variables play a significant role in total number of funders and higher this odds ratio greater is the herding which explains the dependent variables are influenced by funders at different campaign lag periods (Dao et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Herding takes place in the early stages if log of odds of the total funders at t and total funders at t-p is significant and more than 1 with high R-square value and H1 is accepted. In case herding takes place at later stages then H2 is accepted. Accordingly, we run logistic regression for first set of days (day 5 to day 20) and next set of days (day 25 and day 29).\u003c/p\u003e \u003cp\u003eWe use the following model to examine the herding behavior:\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn this model, the relationship between Z and the probability of the event of our interest is given by logit function or log of odds.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eP\u003c/b\u003e \u003csub\u003e \u003cb\u003ei =\u003c/b\u003e \u003c/sub\u003e \u003cb\u003ee\u003c/b\u003e\u003csup\u003e\u003cb\u003eZi\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e/1\u0026thinsp;+\u0026thinsp;e\u003c/b\u003e\u003csup\u003e\u003cb\u003eZi\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e= log(P\u003c/b\u003e\u003csub\u003e\u003cb\u003ei\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e/\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u0026minus;\u003c/b\u003e\u003c/sub\u003e \u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003ei)\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eP\u003c/b\u003e \u003csub\u003e \u003cb\u003ei\u003c/b\u003e \u003c/sub\u003e \u003cb\u003eis the probability of ith event\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eZi is the value of value of the dependent variable\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe value of Z, which is the odds ratio, is expressed as\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eLog Daily funders\u003c/b\u003e \u003csub\u003e \u003cb\u003et\u003c/b\u003e \u003c/sub\u003e \u003cb\u003e(z\u003c/b\u003e\u003csub\u003e\u003cb\u003ei\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e) = α+ꞵ\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e*daily funders\u003c/b\u003e\u003csub\u003e\u003cb\u003etj\u0026minus;p\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e+ꞵ\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e*FAQ\u003c/b\u003e\u003csub\u003e\u003cb\u003et\u0026minus;p\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e+ꞵ\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e*update count\u003c/b\u003e\u003csub\u003e\u003cb\u003etj\u0026minus;p\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e+ꞵ\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e*comments\u003c/b\u003e \u003csub\u003e\u003cb\u003etj\u0026minus;p +\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e+ꞵ\u003c/b\u003e\u003csub\u003e\u003cb\u003e5\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e*projects backed by creator\u003c/b\u003e \u003csub\u003e\u003cb\u003etj\u0026minus;p +\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e+ꞵ\u003c/b\u003e\u003csub\u003e\u003cb\u003e6\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e*projects created\u003c/b\u003e \u003csub\u003e\u003cb\u003etj\u0026minus;p\u003c/b\u003e\u003c/sub\u003e \u003cb\u003ee\u003c/b\u003e\u003csub\u003e\u003cb\u003etj\u0026minus;p\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eIf log of daily number of investors at time t, which is the independent variable is the log of number of accumulated funders from the beginning of the campaign and up to day t-p then herding takes place. Accordingly, if herding takes place at earlier stages then H1 shall be accepted and it occurs at stages later stages then H2 shall be accepted.\u003c/p\u003e \u003cp\u003eAs discussed in literature (Bouri et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), we approach in the following manner, where we find out the odds ratio (Log of odds) where odds ratio is calculated with funders accumulated upto that period with total number of funders in the preceding period. We have segregated the periods as early period, which includes Day1, Day5 and Day10, and the late period includes Day 20, Day 25 and Day 29.\u003c/p\u003e \u003cp\u003e \u003cem\u003eIn case the odds ratio is more than 1 and significant with a higher R-Square and Chi Square, preceding funders in the are influencing the accumulated funders and it shows herding, otherwise herding is absent.\u003c/em\u003e Another important feature where Odds ratio is calculated involves the same pattern of log of accumulated funders at various timelines as dependent variable with various proxies as discussed in literature such as number of updates, number of FAQs, number of comments and projects created and backed by the creators. In this case, if odds ratio is more than 1 and significant then it signifies the role of various factors in the growth of funders.\u003c/p\u003e \u003cp\u003eWe may represent the hypothesis as:\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3. 2 Variables under study\u003c/h3\u003e\n\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003cdiv class=\"Heading\"\u003e3.2.1 Dependent variables\u003c/div\u003e \u003cp\u003eDependent variables refers to the number of funders at different periods starting from Day5, Day10, Day 20, Day 25 and Day 29. Since, funders are in numbers we have segregated the funders in categories such less than 40 funders, more than 40 funders but less than 60 funders, more than 60 funders but less than 80 funders, more than 80 funders but less than 100 funders and more than 100 funders.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003cdiv class=\"Heading\"\u003e3.2.2 Independent variables\u003c/div\u003e \u003cp\u003eWe have classified the independent variables into\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eObservable actions in the project that are easily visible to others connected to the project which includes total number of funders in the preceding period ,number of Frequently Asked Questions(FAQs) available on the web page of campaign, number of campaign updates by creator and number of comments by backers.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eExperience of the creator that are available in the campaign web page such as number of projects backed and created by the creators.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Empirical results","content":"\u003cp\u003eThe results related to herding and descriptives have been presented,\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptives\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptives related to campaigns at various days\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunders (Day 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunders (Day 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e269.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunders (Day 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e317.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2976\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunders (Day 20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e158.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e452.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunders (Day 25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e178.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e528.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunders (Day 29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e216.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e670.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAQ Count (Day 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAQ Count (Day 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAQ Count (Day 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAQ Count (Day 20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAQ Count (Day 25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAQ Count (Day 29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpdate Count (Day 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpdate Count (Day 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpdate Count (Day 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpdate Count (Day 20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpdate Count (Day 25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpdate Count (Day 29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComment Count (Day 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComment Count (Day 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComment Count (Day 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComment Count (Day 20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComment Count (Day 25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComment Count (Day 29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e124.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProjects Created\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProjects Backed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the descriptive statistics for the crowdfunding campaigns across different time points (Days 1, 5, 10, 20, 25, and 29). The number of funders increased over time, starting from a mean of 48.74 on Day 1 to 216.02 on Day 29, indicating substantial campaign growth. FAQ counts remained relatively low throughout the campaign, with mean values around 2\u0026ndash;3. Update counts showed a gradual increase from 0.19 on Day 1 to 2.71 on Day 29, reflecting incremental campaign activity and communication with backers. Comment counts exhibited high variability, with means rising from 3.65 on Day 1 to 22.81 on Day 29 and standard deviations indicating occasional campaigns with very high engagement. The crowdfunding data show that funder activity grows sharply over time, with mean funders increasing from 48.74 on Day 1 to 216.02 on Day 29. This pattern reflects the early-stage dominance of information cascades, where initial backers make contributions based on observable signals rather than peer behavior, as indicated by relatively low median funder counts and minimal FAQ, update, and comment activity. In later stages, herding behavior becomes evident: rising mean funders, comment counts, and update activity suggest that subsequent backers are influenced by the accumulated social proof of campaign popularity, amplifying participation as the campaign progresses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Main analysis of herding\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of results herding dynamics in reward based crowdfunding-using Odds Ratio (OR)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCampaign Day\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFunders Growth Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndependent Variable (Lag)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpdates (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eComments (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFAQs (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eProjects Backed (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eProjects Created (OR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.03315*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.193$*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.4728*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.2173*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.0060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.1638*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.0149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u0026thinsp;\u0026lt;\u0026thinsp;100 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.02837*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.402#*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e125#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.1397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.1563*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.9373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.1141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.0190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u0026thinsp;\u0026lt;\u0026thinsp;80 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.02234*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.1602*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.0609*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.9598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.6338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.0341*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u0026thinsp;\u0026lt;\u0026thinsp;60 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.02240*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.7458*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.8322*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.9493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.1320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.0097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.0335*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.321$*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e133\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.8277*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.1294*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.9041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.0169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.1600*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u0026thinsp;\u0026lt;\u0026thinsp;100 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.0311*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.196#*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.5#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.5004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.9476*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.9558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.0213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.0588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u0026thinsp;\u0026lt;\u0026thinsp;80 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.0274*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.7794*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.7191*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.9176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.8447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u0026thinsp;\u0026lt;\u0026thinsp;60 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.7483*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.6807*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.0165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.1087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.323$*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e138\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.7846*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.6643*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.9154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.0226*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.1521*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u0026thinsp;\u0026lt;\u0026thinsp;100 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.038#*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.4#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.9516*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.8427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.7593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.9725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.8102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u0026thinsp;\u0026lt;\u0026thinsp;80 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.2349*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.3916*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.0175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.1567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u0026thinsp;\u0026lt;\u0026thinsp;60 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.826*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.0915*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.9134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.0244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.0618\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.58*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.296$*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e123\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.5759*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.7209*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.0213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.1397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u0026thinsp;\u0026lt;\u0026thinsp;100 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e27.96*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.944#*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e393#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.7149*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.5718*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.0164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.0111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.9781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u0026thinsp;\u0026lt;\u0026thinsp;80 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e17.02*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.6703*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.5674*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.7623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.0230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.1721*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u0026thinsp;\u0026lt;\u0026thinsp;60 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.4799*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.4795*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.0204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.0212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e9.22*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.265$*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.7\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.0113*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.3377*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.9817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.0205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.1258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u0026thinsp;\u0026lt;\u0026thinsp;100 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e8.36*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.884#*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e374#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.7036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00000000357*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.0342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.0260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.0741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u0026thinsp;\u0026lt;\u0026thinsp;80 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e7.51*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.6837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.2904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.1078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.0299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.1458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u0026thinsp;\u0026lt;\u0026thinsp;60 - \u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.1084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.2074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00000173*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.9940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.9243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e$- R\u003c/b\u003e \u003csup\u003e \u003cb\u003e2\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eand χ\u0026sup2; value for number of updates, number of comments and number of FAQs as independent variable and funders growth category as dependent variable.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003e# - R\u003c/b\u003e \u003csup\u003e \u003cb\u003e2\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eand χ\u0026sup2; value for Funders as independent variable( Day 1,5,10,20 and 25) and funders growth category as dependent variable.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003e*p value less than 0.05\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(Detailed results in Appendix-I)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the results of our different panel regression on the presence of herding dynamics in reward-based crowdfunding based on the number of funders. This model refers to the dependent variables with number of funders segregated at different levels. Furthermore, following the Bouri et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) we replicate models using logistic regression. The reference level is less than 40 funders. \u003cem\u003eThe higher the odds ratio, the more difference with lower level of funders exists and more herding takes place. In order to understand the role of information cascades and herding, we carry our Logistic regression with comments from the backers, FAQs of the campaign by the creators, updates by the creators as independent variables at preceding days and total number of funders on a particular day.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe day wise campaign results may presented as early days of the campaign i.e.5\u0026ndash;10 days, middle of the campaign i.e. 20 days, acceleration phase of the campaign i.e. 25 days and the final closing stage i.e. 29 days.\u003c/p\u003e \u003cp\u003eIn the early stages, funder growth is strongly influenced by initial momentum, with odds ratios (OR\u0026thinsp;=\u0026thinsp;1.02\u0026ndash;1.03) indicating that early contributions increase the likelihood of higher subsequent funder growth. Interactive signals such as updates and comments exhibit very high odds ratios (OR\u0026thinsp;=\u0026thinsp;1.80\u0026ndash;7.70), demonstrating their significant role in boosting funder engagement. Prior creator experience, reflected in projects backed (OR\u0026thinsp;=\u0026thinsp;1.02\u0026ndash;1.16) and projects created (OR\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;1.16), also positively affects funder attraction, suggesting that reputational capital enhances perceived trustworthiness. Odds ratios above unity across growth categories indicate that strong early participation substantially increases the probability of reaching higher funder levels. Updates, in particular, provide new information and reinforce trust, reducing uncertainty, while comments and FAQs further sustain momentum. The models\u0026rsquo; explanatory power (R\u0026sup2; = 0.19\u0026ndash;0.40) confirms that early-stage engagement is a critical determinant of campaign outcomes, consistent with prior research highlighting the influence of cascades during the initial phases (Vismara, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xiao et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Garnica et al., 2024).\u003c/p\u003e \u003cp\u003eDuring the mid-campaign stage (Day 20), funder counts show a flat effect (OR\u0026thinsp;\u0026asymp;\u0026thinsp;1.00, non-significant), while updates (OR\u0026thinsp;\u0026asymp;\u0026thinsp;1.78\u0026ndash;2.23) and comments (OR\u0026thinsp;\u0026asymp;\u0026thinsp;1.84\u0026ndash;2.66) emerge as the primary drivers of growth. At this stage, backers rely more on creator-provided information rather than sheer funder numbers, with FAQs playing a negligible role. Creator experience, captured by projects created (OR\u0026thinsp;=\u0026thinsp;1.15) and backed (OR\u0026thinsp;=\u0026thinsp;1.02), remains significant, reinforcing trust and mitigating information asymmetry. This pattern indicates that information cascades intensify, as new backers imitate earlier participants, laying the foundation for later-stage herding.\u003c/p\u003e \u003cp\u003eIn the acceleration phase (Day 25), funder counts demonstrate explosive growth (OR\u0026thinsp;=\u0026thinsp;17.0\u0026ndash;31.5), signaling that campaigns achieving critical mass attract disproportionate attention. Updates and comments remain significant (OR\u0026thinsp;\u0026asymp;\u0026thinsp;1.57\u0026ndash;1.71) but their relative influence declines compared to the dominant effect of a large funder base. Herding peaks here, with rapid amplification driven by social proof. By the campaign closing phase (Day 29), funder ORs remain strong (\u0026asymp;\u0026thinsp;7.5\u0026ndash;9.2) but growth begins to slow, reflecting saturation. Updates continue to play a key role in sustaining momentum (OR\u0026thinsp;\u0026asymp;\u0026thinsp;2.0), while comments show mixed effects and FAQs become largely irrelevant. Creator experience maintains consistent though moderate effects (OR\u0026thinsp;=\u0026thinsp;1.13\u0026ndash;1.17), ensuring trust in campaigns led by experienced creators.\u003c/p\u003e \u003cp\u003eOverall, these results illustrate a temporal shift in drivers of funder growth. Early-stage contributions and quality signals (updates, comments, creator experience) drive information cascades, while later stages are dominated by herding effects, where campaigns with large funder bases attract further support. This pattern aligns with prior literature highlighting the stage-wise dynamics of crowdfunding, where information cascades precede herding behavior (Vismara, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Garnica et al., 2024; Kapounek \u0026amp; Kucerova, 2019; Comeig et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Further analysis of herding\u003c/h2\u003e \u003cp\u003eIn the first step regarding analysis of herding, we used the theoretical framework developed in various literatures where information cascades takes place at the early stages and herding at the later stages. We have used several proxies to determine how the information cascades are developed in early stages. However, in order to confirm the fact that information cascades occur at the early stages and herding later, we use the Machine Learning methods. From the literatures, it is evident that there are various models for predictions of information cascades, which includes autoregressive models, decision tree, logistic regression, random forests and support vector mechanisms (Zhou et al., 2021). The use of ML methods have been a robustness test in crowdfunding (Yang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Elitzur and Solodoha, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) but mostly related to equity crowdfunding. The use of Linear Discriminant Analysis (LDA) for prediction of factors contributing success in RBCF (Oduro et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have been demonstrated and the LDA approach is having more accuracy except the Linear regression models.\u003c/p\u003e \u003cp\u003eBased on the literatures we use the LDA model to find out the role of updates and comments as well as projects created and backed by the creator. The results are presented in the tables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of results of LDA using funders as dependent variables and other variables (Project specific)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay(Dependent variables)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFunders category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrior Probability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDay(Independent variable)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFunders\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFAQ_Count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpdate_Count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eComment_Count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLD1 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLD1 Centroid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLD2 Centroid\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDay1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e166.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e83.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e91.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;1.3574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u0026thinsp;\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.2718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u0026thinsp;\u0026lt;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u0026thinsp;\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.5844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDay5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e390.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e88.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u0026thinsp;\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u0026thinsp;\u0026lt;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u0026thinsp;\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDay10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e365.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e83.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u0026thinsp;\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u0026thinsp;\u0026lt;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u0026thinsp;\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDay20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e444.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e49.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e84.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u0026thinsp;\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u0026thinsp;\u0026lt;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u0026thinsp;\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDay25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e465.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e51.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e78.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u0026thinsp;\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;1.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;1.766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u0026thinsp;\u0026lt;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u0026thinsp;\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of results of LDA using funders as dependent variables and other variables (creator specific)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay(Dependent variables)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFunders Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrior Probability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProjects Created (Mean)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProjects Backed (Mean)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLD1 Loading: Projects Created\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLD1 Loading: Projects Backed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGroup Centroid LD1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGroup Centroid LD2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u0026thinsp;\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u0026thinsp;\u0026lt;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.9501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u0026thinsp;\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.7179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u0026thinsp;\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u0026thinsp;\u0026lt;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.661\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u0026thinsp;\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.7083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u0026thinsp;\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u0026thinsp;\u0026lt;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u0026thinsp;\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.9716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u0026thinsp;\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.4230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u0026thinsp;\u0026lt;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u0026thinsp;\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u0026thinsp;\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-1.845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u0026thinsp;\u0026lt;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u0026thinsp;\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003e(Detailed results in Appendix-II\u003c/b\u003e)\u003c/p\u003e\u003cp\u003eLinear Discriminant Analysis across Days 5, 10, 20, 25, and 29 confirms that early and sustained engagement signals\u0026mdash;particularly updates, comments, and creator experience\u0026mdash;strongly predict funder growth. On Day 5, most campaigns (\u0026lt;\u0026thinsp;40 funders, 57.9%) exhibited low engagement, while high-performing campaigns (\u0026gt;\u0026thinsp;100 funders) displayed markedly higher activity (~\u0026thinsp;167 funders, ~\u0026thinsp;5 updates, ~\u0026thinsp;84 comments). LD1 accounted for 91.3% of discrimination, indicating that early signals effectively distinguish highly successful campaigns from low-performing ones.\u003c/p\u003e \u003cp\u003eBy Day 10, high-success campaigns (\u0026gt;\u0026thinsp;100 funders) increased to 23.3%, anchored by active commenting and updates (~\u0026thinsp;391 funders, ~\u0026thinsp;2 updates, ~\u0026thinsp;38 comments), with LD1 explaining\u0026thinsp;\u0026gt;\u0026thinsp;83% of discrimination. Day 20 LDA confirmed that updates and comments at Day 10 remain strong predictors, with creator experience reinforcing credibility and encouraging herding. In the acceleration phase (Day 25), LD1 captured\u0026thinsp;~\u0026thinsp;85% of discrimination, showing sustained momentum and strong herding effects among campaigns achieving\u0026thinsp;\u0026gt;\u0026thinsp;100 funders. At campaign closure (Day 29), Day 25 funders continued to predict success (LD1\u0026thinsp;\u0026asymp;\u0026thinsp;78%), with high-performing campaigns maintaining active engagement, while mid-tier and low-performing campaigns remained less distinct.\u003c/p\u003e \u003cp\u003eOverall, these results indicate a temporal shift in drivers of funder growth. Early-stage contributions and quality signals initiate information cascades, which evolve into herding behavior as campaigns progress. High-performing campaigns consistently exhibit strong engagement across updates, comments, and creator experience, whereas mid-tier campaigns with weaker early signals show overlapping centroids and less predictable outcomes. These findings underscore the critical role of early and sustained engagement in driving reward-based crowdfunding success.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussions","content":"\u003cp\u003eThe implications of the findings have been summarized as follows.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Theoretical Implications\u003c/h2\u003e \u003cp\u003eThis paper contributes to the literature on reward-based crowdfunding by highlighting how signaling and herding dynamics influence the success of campaigns. The results indicate that early backers tend to make informed decisions based on publicly available quality signals, drawing on their abilities and expertise to evaluate new projects. The findings support the two-stage model of collective decision-making in crowdfunding:\u003c/p\u003e \u003cp\u003e \u003cb\u003ea) Information cascades\u003c/b\u003e dominate in the \u003cem\u003eearly stages\u003c/em\u003e, where backers imitate prior funders based on visible quality signals (updates, comments, creator\u0026rsquo;s experience early contributions).\u003c/p\u003e\u003cp\u003eb) \u003cb\u003eHerding behavior\u003c/b\u003e emerges in the \u003cem\u003elater stages\u003c/em\u003e, once campaigns achieve momentum, where funder counts themselves drive exponential growth.\u003c/p\u003e \u003cp\u003eThis duality aligns with Kapounek \u0026amp; Kucerova (2019), Comeig et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and Garnica et al. (2024), who suggest cascades transform into herding as campaigns mature.\u003c/p\u003e \u003cp\u003eThe sharp increase in odds ratios for funder counts during the acceleration phase(later phases) indicates a threshold have been reached .Herding is not linear\u0026mdash;it becomes dominant only after campaigns accumulate a sufficiently large base of early backers. This reinforces theories of non-linear dynamics in collective funding, where popularity cues trigger disproportionately large behavioral responses.\u003c/p\u003e \u003cp\u003eUpdates, comments, and FAQs play a catalytic role in shaping cascades that later transform into herding. The results imply that herding is not a purely irrational \u0026ldquo;blind following\u0026rdquo; phenomenon, but is anchored in quality signals that reduce uncertainty in early stages. Even during strong herding phases; updates and comments remain significant\u0026mdash;though secondary. \u003cem\u003eThis demonstrates that herding does not substitute quality signals\u003c/em\u003e, \u003cem\u003ebut rather interacts with them to reinforce growth\u003c/em\u003e. Theoretically, this extends signaling theory by showing how dynamic interactions between quality signals and social proof sustain momentum. Herding is not uniformly beneficial. The creator\u0026rsquo;s experience in form of projects backed and projects created are important particularly during the early stages of the campaign, which also acts as quality signals during funding by early backers. This highlights that \u003cb\u003eherding requires consistent reinforcement\u003c/b\u003e, not just early momentum.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Implications for entrepreneurs and policy makers\u003c/h2\u003e \u003cp\u003eFor Entrepreneurs (Campaign Creators)\u003c/p\u003e \u003cp\u003eSince early funder counts drive information cascades, creators must focus on securing strong initial backer support (friends, family, early investors) within the first 5\u0026ndash;10 days. Pre-launch marketing, warm leads, and exclusive early-bird rewards help trigger cascades. Updates, comments, and FAQs act as trust-building mechanisms. Posting frequent, transparent updates and encourage active commenting from early backers. FAQs should be used mainly in the early and middle stages, but are less useful toward the end. By Day 20, funder count alone is no longer a predictor of growth\u0026mdash;backers look for creator responsiveness and community activity.\u003c/p\u003e \u003cp\u003eOnce critical mass is achieved, campaigns can experience exponential herding effects. Time strategic updates (stretch goals, bonus rewards, partnerships) to maximize late-stage inflows. In closing stages, updates are critical to maintain momentum and trigger last-minute contributions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFor policy makers\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSince cascades rely on visible signals, platforms should ensure transparent and accessible campaign data (funding progress, updates, backer interactions). Policymakers can encourage platforms to design mechanisms (e.g., bonus rewards, matching grants) that reward early contributions, as these have disproportionate influence. Herding can lead to overfunding of popular but low-quality projects, creating systemic risks. Policymakers may consider disclosure requirements (project progress, creator history, financial details) to ensure herding is supported by quality signals. To mitigate risks of misinformed herding, rules could require platforms to flag campaign credibility indicators (verification badges, creator reputation scores).\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Limitations and Scope for Future Research","content":"\u003cp\u003eThis study considered primarily numerical variables, leaving scope for the inclusion of categorical variables related to the creator\u0026mdash;such as age, gender, and professional experience (Rossi et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u0026mdash;in future research.\u003c/p\u003e \u003cp\u003eAnother promising avenue is to examine manipulative practices that may distort funding patterns (Meoli \u0026amp; Vismara, 2021). For instance, early backers may initially invest heavily to signal project quality, only to withdraw later after attracting a large pool of late backers. Similarly, creators may engage in impression management (Gleasure, 2015), which could amplify herding effects.\u003c/p\u003e"},{"header":"7. Conclusions","content":"\u003cp\u003eThis study examines the temporal dynamics of reward-based crowdfunding and shows how information cascades and herding evolve across campaign stages. In the early phase, strong initial participation and visible quality signals\u0026mdash;particularly updates and comments along with creator\u0026rsquo;s experience in form of projects backed \u0026mdash;create information cascades that shape perceptions of project quality and encourage further contributions. By the midpoint, the marginal impact of funder counts diminishes, while sustained engagement through creator updates and backer comments becomes the primary determinant of growth. In the acceleration phase, once campaigns achieve threshold, herding effects dominate, with funder base size driving exponential growth while updates and comments provide supportive but secondary reinforcement. In the closing stage, growth slows naturally, yet timely updates and urgency cues remain critical to sustaining momentum, whereas FAQs lose relevance and comments show mixed influence.\u003c/p\u003e \u003cp\u003eThe results highlight a dual-phase process: cascades in the early stages and herding in later stages, consistent with theories of social proof and collective decision-making. Practically, campaign creators should prioritize early momentum, continuous engagement, and effective end-stage communication, while policymakers and platforms should enhance transparency, promote interaction, and safeguard against herd-driven misallocations. Overall, crowdfunding success emerges from the interplay of early signals, engagement, and popularity, offering valuable insights for both theory and practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis research adheres to all ethical and confidentiality requirement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding:\u003c/em\u003e\u003c/strong\u003e This research has not received any external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflicts of Interest/Competing Interests\u003c/em\u003e\u003c/strong\u003e: The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials:\u003c/em\u003e\u003c/strong\u003e The datasets used and/or analyzed in this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor Contributions:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eAll authors have equally participated in the research. All authors have read and agreed to the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgement:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe authors are sincerely thankful to all anonymous referees whose valuable comments help to improve the quality of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHuman Ethics and Consent to Participate declarations\u003c/em\u003e\u003c/strong\u003e: \u0026lsquo;Not applicable\u0026rsquo;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhlers GKC, Cumming D, G\u0026uuml;nther C, Schweizer D (2015) Signaling in equity crowdfunding. Entrepreneurship Theory Pract 39(4):955\u0026ndash;980. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/etap.12157\u003c/span\u003e\u003cspan address=\"10.1111/etap.12157\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkerlof GA (1970) The market for lemons: Quality uncertainty and the market mechanism. Q J Econ 84(3):488\u0026ndash;500\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAleksina N, Akulenka S, Lubl\u0026oacute;y \u0026Aacute; (2019) Success factors of crowdfunding campaigns in medical research: Perceptions and reality. Drug Discovery Today 24(7):1413\u0026ndash;1420. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.drudis.2019.05.012\u003c/span\u003e\u003cspan address=\"10.1016/j.drudis.2019.05.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmirat A, Alwafi W (2020) Does herding behavior exist in cryptocurrency market? Cogent Econ Finance 8(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/23322039.2020.1735680\u003c/span\u003e\u003cspan address=\"10.1080/23322039.2020.1735680\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnglin AH, Short JC, Drover W, Stevenson RM, McKenny AF, Allison TH (2018) The power of positivity? The influence of positive psychological capital language on crowdfunding performance. J Bus Ventur 33(4):470\u0026ndash;492. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbusvent.2018.03.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jbusvent.2018.03.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Aring;stebro T, Fern\u0026aacute;ndez M, Lovo S, Vulkan N (2024) Herding in equity crowdfunding. RAND J Econ 55(3):403\u0026ndash;441\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAstebro TB, Fern\u0026aacute;ndez, Sierra (2019) Manuel and Lovo, Stefano and Vulkan, Nir, Herding in Equity Crowdfunding18, (June Paris December 2018 Finance Meeting EUROFIDAI - AFFI, RAND Journal of Economics, Forthcoming, Available at SSRN: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ssrn.com/abstract=3084140\u003c/span\u003e\u003cspan address=\"https://ssrn.com/abstract=3084140\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e or \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.2139/ssrn.3084140\u003c/span\u003e\u003cspan address=\"10.2139/ssrn.3084140\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelleflamme P, Lambert T, Schwienbacher A (2014) Crowdfunding: Tapping the right crowd. J Bus Ventur 29(5):585\u0026ndash;609. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbusvent.2013.07.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jbusvent.2013.07.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlohm I, Antretter T, Sir\u0026eacute;n C, Grichnik D, Wincent J (2022) It\u0026rsquo;s a people\u0026rsquo;s game, isn\u0026rsquo;t it?! A comparison between the investment returns of business angels and machine learning algorithms. Entrepreneurship Theory Pract 46(4):1054\u0026ndash;1091. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1042258720945206\u003c/span\u003e\u003cspan address=\"10.1177/1042258720945206\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouri E, Gupta R, Roubaud D (2019) Herding behaviour in cryptocurrencies. Finance Res Lett 29:216\u0026ndash;221. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.frl.2018.07.008\u003c/span\u003e\u003cspan address=\"10.1016/j.frl.2018.07.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eButtic\u0026egrave; V, Di Pietro F, Tenca F (2020) Is equity crowdfunding always good? Deal structure and the attraction of venture capital investors. J Corp Finance 65:101773. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jcorpfin.2020.101773\u003c/span\u003e\u003cspan address=\"10.1016/j.jcorpfin.2020.101773\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan CSR, Parhankangas A, Sahaym A, Oo P (2020) Bellwether and the herd? Unpacking the U-shaped relationship between prior funding and subsequent contributions in reward-based crowdfunding. J Bus Ventur 35(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 105934. https://doi.org/10.1016/j.jbusvent.2019.04.002\u003c/span\u003e\u003cspan address=\"Article 105934. 10.1016/j.jbusvent.2019.04.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCillo V, Petruzzelli AM, Ardito L, Del Giudice M (2019) Understanding sustainable innovation: A systematic literature review. Corp Soc Responsib Environ Manag 26(5):1012\u0026ndash;1025\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColombo MG, Franzoni C, Rossi-Lamastra C (2015) Internal social capital and the attraction of early contributions in crowdfunding. Entrepreneurship Theory Pract 39(1):75\u0026ndash;102\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eComeig I, Mesa-V\u0026aacute;zquez E, Sendra-Pons P, Urbano A (2020) Rational herding in reward-based crowdfunding: An MTurk experiment. Sustainability 12(23):9827\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCordova A, Dolci J, Gianfrate G (2015) The determinants of crowdfunding success: Evidence from technology projects. Procedia \u0026ndash; Social Behav Sci 181:115\u0026ndash;124. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.sbspro.2015.04.872\u003c/span\u003e\u003cspan address=\"10.1016/j.sbspro.2015.04.872\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCourtney C, Dutta S, Li Y (2017) Resolving information asymmetry: Signaling, endorsement, and crowdfunding success. Entrepreneurship Theory Pract 41(2):265\u0026ndash;290. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/etap.12267\u003c/span\u003e\u003cspan address=\"10.1111/etap.12267\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrosetto P, Regner T (2014) Crowdfunding: Determinants of success and funding dynamics. Jena Economic Res Papers, 2014\u0026ndash;2035\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai H, Zhang DJ (2019) Prosocial goal pursuit in crowdfunding: Evidence from Kickstarter. J Mark Res 56(3):498\u0026ndash;517. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0022243718821697\u003c/span\u003e\u003cspan address=\"10.1177/0022243718821697\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDao D, Nguyen T, Andrikopoulos P (2024) Herding dynamics and multidimensional uncertainty in equity crowdfunding: The impacts of information sources. Inf Manag 61(1):103889. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.im.2023.103889\u003c/span\u003e\u003cspan address=\"10.1016/j.im.2023.103889\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng L, Ye Q, Xu D, Sun W, Jiang G (2022) A literature review and integrated framework for the determinants of crowdfunding success. Financial Innov 8(1):1\u0026ndash;70\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhochak M, Pahal S, Doliya P (2022) Predicting startup valuation: A deep learning approach. Venture Capital 26(1):75\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13691066.2022.2161968\u003c/span\u003e\u003cspan address=\"10.1080/13691066.2022.2161968\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElitzur R, Katz N, Muttath P, Soberman D (2024) The power of machine learning methods to predict crowdfunding success: Accounting for complex relationships efficiently. J Bus Venturing Des 3:100022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbvd.2024.100022\u003c/span\u003e\u003cspan address=\"10.1016/j.jbvd.2024.100022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElitzur R, Solodoha E (2021) Does gender matter? Evidence from crowdfunding. J Bus Venturing Insights 16:e00268. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbvi.2021.e00268\u003c/span\u003e\u003cspan address=\"10.1016/j.jbvi.2021.e00268\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerrati F, Muffatto M (2021) Reviewing equity investors\u0026rsquo; funding criteria: A comprehensive classification and research agenda. Venture Capital 23(2):157\u0026ndash;178. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13691066.2021.1883211\u003c/span\u003e\u003cspan address=\"10.1080/13691066.2021.1883211\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHadačov\u0026aacute; D, Maličk\u0026aacute; L (2022) Reward-Based Crowdfunding. How to Make it Work? \u003cem\u003eTheory, Methodology\u003c/em\u003e. Pract \u0026ndash; Rev Bus Manage 18(01):45\u0026ndash;59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18096/TMP.2022.01.04\u003c/span\u003e\u003cspan address=\"10.18096/TMP.2022.01.04\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHagawe HM, Mobarek A, Hanuk A, Jamal A (2023) A unique business model for microfinance institution: the case of Assadaqaat Community Finance (ACF). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/23311975.2022.2135202\u003c/span\u003e\u003cspan address=\"https://doi.org/10.1080/23311975.2022.2135202\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cogent Bus Manag \u003cem\u003e10\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHornuf L, Schwienbacher A (2018) Market mechanisms and funding dynamics in equity crowdfunding. J Corp Finance 50:556\u0026ndash;574. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jcorpfin.2017.08.009\u003c/span\u003e\u003cspan address=\"10.1016/j.jcorpfin.2017.08.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Y, Uy MA, Liu C, Foo MD, Li ZA (2023) Visual totality of rewards-based crowdfunding pitch videos: Disentangling the impact of peak negative affective visual expression on funding outcomes. J Bus Ventur 38:106318. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbusvent.2023.106318\u003c/span\u003e\u003cspan address=\"10.1016/j.jbusvent.2023.106318\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang S, Pickernell D, Battisti M et al (2022) Signalling entrepreneurs\u0026rsquo; credibility and project quality for crowdfunding success: cases from the Kickstarter and Indiegogo environments. Small Bus Econ 58:1801\u0026ndash;1821. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11187-021-00477-6\u003c/span\u003e\u003cspan address=\"10.1007/s11187-021-00477-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang Y, Ho YC, Yan X, Tan Y (2018) Investor platform choice: Herding, platform attributes, and regulations. J Manage Inform Syst 35(1):86\u0026ndash;116\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang Y, Ho YC, Yan X, Tan Y (2022) What\u0026rsquo;s in a username? The effect of perceived anonymity on herding in crowdfunding. Inform Syst Res 33(1):1\u0026ndash;17\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaartemo V (2017) The elements of a successful crowdfunding campaign: A systematic literature review of crowdfunding performance. Int Rev Entrepreneurship 15:291\u0026ndash;318\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKapounek S, Kucerov\u0026aacute; Z (2019) Overfunding and signaling effects of herding behavior in crowdfunding (CESifo Working Paper No. 7973). CESifo\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLitjens G, S\u0026aacute;nchez C, Timofeeva N et al (2016) Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep 6:26286. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep26286\u003c/span\u003e\u003cspan address=\"10.1038/srep26286\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMollick, Ethan The dynamics of crowdfunding: An exploratory study,Journal of Business Venturing, 29, Issue 1,2014,Pages 1\u0026ndash;16,ISSN 0883\u0026ndash;9026,\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbusvent.2013.06.005\u003c/span\u003e\u003cspan address=\"10.1016/j.jbusvent.2013.06.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoritz A, Block JH (2016) Crowdfunding: A Literature Review and Research Directions. In: Br\u0026uuml;ntje D, Gajda O (eds) Crowdfunding in Europe. FGF Studies in Small Business and Entrepreneurship. Springer, Cham. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-18017-5_3\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-18017-5_3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOduro MS, Yu H, Huang H (2022) Predicting the entrepreneurial success of crowdfunding campaigns using model-based machine learning methods. Int J Crowd Sci 6(1):7\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.26599/IJCS.2022.9100003\u003c/span\u003e\u003cspan address=\"10.26599/IJCS.2022.9100003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetit A, Wirtz P (2022) Experts in the crowd and their influence on herding in reward-based crowdfunding of cultural projects. Small Bus Econ 58:419\u0026ndash;449. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11187-020-00424-x\u003c/span\u003e\u003cspan address=\"10.1007/s11187-020-00424-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePosch L, Mladenow A, Strauss C (2022) Reward-based Crowdfunding: successful signaling from an entrepreneur. \u003cem\u003eCEUR Workshop Proceedings\u003c/em\u003e, \u003cem\u003e3171\u003c/em\u003e, 999\u0026ndash;1014\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRalcheva A, Roosenboom P Forecasting Success in Equity Crowdfunding (January 2019). Available at SSRN: https://ssrn.com/abstract=3260140 or \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.2139/ssrn.3260140\u003c/span\u003e\u003cspan address=\"10.2139/ssrn.3260140\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez-Garnica G, Guti\u0026eacute;rrez-Urtiaga M, Tribo JA (2025) Signaling and herding in reward-based crowdfunding. Small Bus Econ 64:889\u0026ndash;916. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11187-024-00933-z\u003c/span\u003e\u003cspan address=\"10.1007/s11187-024-00933-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRossi A, Vanacker T, Vismara S (2021) Equity crowdfunding: New evidence from US and UK markets. Rev Corp Finance 1:407\u0026ndash;453. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1561/114.00000009\u003c/span\u003e\u003cspan address=\"10.1561/114.00000009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScharfstein DS, Stein JC (1990) Herd behavior and investment. Am Econ Rev, 465\u0026ndash;479\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShneor R, Vik AA (2020) Crowdfunding success: a systematic literature review 2010\u0026ndash;2017. Baltic J Manage 15(2):149\u0026ndash;182\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpyrou S (2013) Herding in financial markets: a review of the literature. Rev Behav Finance 5(2):175\u0026ndash;194\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUsman SM, Bukhari FAS, You H, Badulescu D, Gavrilut D (2020) The Effect and Impact of Signals on Investing Decisions in Reward-Based Crowdfunding: A Comparative Study of China and the United Kingdom. J Risk Financial Manage 13(12):325. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jrfm13120325\u003c/span\u003e\u003cspan address=\"10.3390/jrfm13120325\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVismara S (2018) Information Cascades among Investors in Equity Crowdfunding. Entrepreneurship Theory Pract 42(3):467\u0026ndash;497. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/etap.12261\u003c/span\u003e\u003cspan address=\"10.1111/etap.12261\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVismara S (2016) Equity retention and social network theory in equity crowdfunding. Small Bus Econ 46:579\u0026ndash;590. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11187-016-9710-4\u003c/span\u003e\u003cspan address=\"10.1007/s11187-016-9710-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang W, He L, Wu YJ, Goh M (2021) Signaling persuasion in crowdfunding entrepreneurial narratives: The subjectivity vs objectivity debate. Comput Hum Behav 114:106576. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chb.2020.106576\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2020.106576\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWohlgemuth,Veit Elisabeth SC, Berger M, Wenzel More than just financial performance: Trusting investors in social trading. J Bus Res, 69, Issue 11,2016,Pages 4970\u0026ndash;4974,ISSN 0148\u0026ndash;2963,\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbusres.2016.04.061\u003c/span\u003e\u003cspan address=\"10.1016/j.jbusres.2016.04.061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao S, Ho YC, Che H (2021) Building the momentum: Information disclosure and herding in online crowdfunding. Prod Oper Manage 30(9):3213\u0026ndash;3230\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Xin J, Zeng Y et al (2025) Signaling and perceiving on equity crowdfunding decisions \u0026mdash; a machine learning approach. Small Bus Econ 65:315\u0026ndash;356. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11187-024-00991-3\u003c/span\u003e\u003cspan address=\"10.1007/s11187-024-00991-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Liu P (2012) Rational herding in microloan markets. Manage Sci 58(5):892\u0026ndash;912. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1287/mnsc.1110.1459\u003c/span\u003e\u003cspan address=\"10.1287/mnsc.1110.1459\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou F, Xu X, Trajcevski G, Zhang K (2022) A survey of information cascade analysis: Models, predictions, and recent advances. ACM-CSUR 54(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 27. https://doi.org/10.1145/3433000\u003c/span\u003e\u003cspan address=\"Article 27. 10.1145/3433000\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Z, Liu J, Zhang M (2022) Rethinking investors' herding behavior under the conditions of reward-based crowdfunding platform. Industrial Manage Data Syst 122(12):2762\u0026ndash;2782\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Reward based crowdfunding, Information cascade, herding behaviour, Signaling","lastPublishedDoi":"10.21203/rs.3.rs-8601520/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8601520/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the interaction between quality signals and herding behaviour in reward-based crowdfunding and how this interaction generates information cascades. Using live campaign data from Kickstarter comprising 1,056 observations between March and April 2025, the findings reveal heterogeneous backer responses across campaign stages. In the early phase, informed backers rely on observable quality signals such as creator updates, project comments, and creator experience (prior projects created and backed). During the middle phase, these signals become critical in sustaining campaign credibility and driving progress toward the funding threshold. In contrast, late-stage backers exhibit strong herding tendencies and contribute based on accumulated funding momentum rather than signal quality. The study demonstrates that backers self-select into early or late contributors depending on their information-processing ability, leading to information cascades that reduce asymmetric information. The findings offer implications for campaign creators seeking strategic signalling and regulators aiming to protect uninformed contributors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL: G30, G32\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"Crowdfunder’s Paradox and Herding dynamics in Reward Based Crowdfunding","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 12:56:10","doi":"10.21203/rs.3.rs-8601520/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fb279d4f-563f-4249-9354-37a24cc108ac","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T21:11:05+00:00","index":15,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-27T12:56:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 12:56:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8601520","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8601520","identity":"rs-8601520","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}