“Funding acquisition drivers for new venture firms: Diminishing value of human capital signals in early rounds of funding”

Multiple factors such as human capital, amount raised in the first round, innovation etc. have an impact on the funding prospect of new ventures. This paper explored the influencing factors that drive multiple rounds of funding for new venture firms and provided a much broader perspective of funding drivers during the early stages of the new venture firm. Using signalling theory and human capital theory, this paper analyzed signals that influence the acquisition of funds in the first round and whether those signals persisted for the second and third rounds of funding when information asymmetries between the investors and new venture firms reduce. This study disentangled the signalling effects of the human capital factors across three funding rounds and proved the diminishing value of signals across each subsequent round of funding. Finding showed that the signal effect from premier institution education was the only human capital signal that persisted across each round of funding, while other signals did not persist beyond the first round of funding. In addition, new venture firms with founders educated from premier educational institutions were able to attract more investors and close more funding rounds. This study also proved that the amount raised in the first round of funding positively impacted the amounts raised in the second and third rounds stressing its importance for new venture firms. Empirical demonstration of the propositions was done with 156 new venture firms in India, the fastest growing and third largest startup ecosystem in the world.


INTRODUCTION
Funding is a multi-stage process. The characteristics of the venture at the early stage are very different from that of a venture during the growth stage or Initial Public Offering (IPO). The venture undergoes a transformation as it evolves and obtains external financing, acquires customers and grows in terms of revenues and employees. Existing literature on entrepreneurship explored the relationships among the various human capital factors and the funding received by new ventures at a single stage/round (Klotz et al., 2014;Zimmerman, 2008). Past research only looked at funding as a one-time event (Baum & Silverman, 2004;Zimmerman, 2008).
This study argued the importance of analyzing funding over multiple stages and not just at one stage as demonstrated by existing literature. Using signaling theory, this study disentangled the effects of the various human capital factors not only over the first round of funding, but also over three rounds of funding.
Past studies failed to analyze the importance of the amount of funding received in the first round and if that could act as a signal to future investors (Klotz et al., 2014;Zimmerman, 2008). Further, the quality of education and its impact as the signaling mechanism were not analyzed in the existing literature (Hsu, 2007;Klotz et al., 2014).
Linking human capital theory with signaling theory, this paper extended the research by providing contribution to new ventures in three distinct ways. First, this being one of the first studies in evaluating the diminishing effect and the persistence/non-persistence of human capital signals over multiple rounds of funding, this paper explored the various signals influencing the investments raised across each round of funding and human capital signals in-depth across multiple rounds of funding.
Second, the existing research analyzed either the education level or years of education (Hsu, 2007;Colombo & Grilli, 2007) of new venture firm founders, but did not analyze the signal emanated from the quality of education as obtained from premier institutions by these founders. Important contribution of this paper is introducing a new quality dimension for education, which is graduation from a premier institution and measuring its signal impact over multiple rounds of funding.
Third, this paper explicitly analyzed the effect and influence of the signal from the amount raised in the first round of funding and its impact on subsequent two rounds of funding. This is an essential indicator to new venture firms, as it highlights the importance of the amount of funding raised in the first round irrespective of the need.
From 2010 to 2015, the period of this study, investments from local and international investors in new venture firms in India grew at an unprecedented rate making India the third largest and one of the fastest growing startup ecosystems in the world (Thorton & Assocham, 2016), hence highlighting the timeliness of this study. Both fast emerging startup ecosystems and mature ecosystems will benefit from unique insights provided in this paper.
Next section deals with the literature review and hypotheses development followed by methods section and then concludes with explanation of results followed by discussion, implications and limitations of this study.

Signaling theory for funding acquisition
New ventures face difficult founding conditions and face a gargantuan task of building and scaling their firms (Stinchcombe, 1965). Funding is an important pillar of resource acquisition and is one of the most important constituents that sustains and helps a new venture during the initial and future growth stages (Cooper et al., 1994). Raising funds for one's venture is a prolonged and arduous process (Rédis, 2010). This is due to information asymmetries between the founder and the investor (Connelly et al., 2011). Signaling theory is based on the foundation of reducing information asymmetries between two parties (Connelly et al., 2011

Human capital factors affecting funding acquisition
Human capital theory relies on the premise of the capabilities of the founders involved in a new venture firm. Since the new venture has many uncertainties associated with it, investors look for things that are more certain about the firm. From this perspective, past industry experience provides a guiding post for future performance of the new venture. In a study across multiple industries in the Silicon Valley, it was found that the prominence of prior employers of founding team members increased the likelihood of obtaining external investment (Burton et al., 2002). Investors favored teams with strong expertise in the industry, social ties with important stakeholders and management experience in leading an organization (Zimmerman, 2008). However, other stud-ies found negative correlation between industry experience and attraction of investors because of founders' resistance to advice (Barney et al., 1996).
Investors preferred founders with good education (Zimmerman, 2008). However, other studies such as Amason et al. (2006) found no direct relationship between education (level, specialization etc.) and firm performance. This difference in view of past studies is due to the characteristic of the education variable-level versus quality of education obtained.
Prior startup experience, considered an important signal for future investors, gives a way for investors to assess the founders' capabilities and performance in similar situations (Hsu, 2007). Experiential learning and exposure to investors associated with previous firm should aid in raising funds for the current firm (Reuber, 1994). However, Cassar (2014) found no significant evidence linking prior startup experience and performance of firms.
It is vital to assess the signaling mechanisms of the various human capital factors and their influence in raising the first round of funding to have a clearer understanding of the possible gaps in literature. Argument which is put forth in this paper is that human capital factors are more important only during the first round of funding and their significance will reduce during the later stages of funding, as investors look for other signals during these stages. The persistence of the human capital factors decreases or has no impact in the later stages.

Premier institution
Formal education is considered an added advantage and it has direct influence on the prospects of the firm and the valuation at IPO (Zimmerman, 2008). But, Amason et al. (2006) have found conflicting results between performance and education. Because existing studies on signaling theory have looked at education from the perspective of the level and number of years of education obtained, they found no relationship between performance and education. A typical coding format has been used in these studies coding each echelon of education obtained.
The existing literature on signaling mechanism for education variable has not measured the quality of education, even though Becker (1993) found that individuals with better degree credentials convey information about differences in abilities, persistence and other important traits. Quality of education mattered and CEOs that graduated from the top-20 US universities were able to realize superior performance (King et al., 2016). A more indepth view of the quality of education measured by the influence of premier educational institutions is necessary.
In the United States, the Ivy League schools are considered premier educational institutions of the country (Clement, 1975;Tapper, 2009 These schools also act as a breeding ground for strong social networks with deep connections within the industry and investor circles (Miller et al., 2015). It is not surprising that many celebrated CEOs have strong linkages to the premier institutions as many of them are graduates from these institutions (Miller et al., 2015). Graduates of premier institutions hence have the entry route to the inner sanctums of the key capitalist institutions (Tapper, 2009). Along the same lines, Maidique (1985) found that founders from high quality institutions have a positive influence on the success of their firms.
In an increasingly global workplace, continuing the education in other countries may give greater global awareness and engagement, openness to a variety of perspectives on international and cultural issues, and many other things that will enable students to form human capital after graduation (Paige et al., 2010). Studying abroad may allow students to form human capital in ways not possible at home and may enable them to earn higher incomes (Schmidt & Pardo, 2017). Studying abroad and hence gaining the requisite human capital has been considered factors for premier institute education.

Industry experience
Entrepreneurship entails working with unfamiliar issues and situations that carry a high amount of risk as the outcomes could vary (Kirzner, 1997). Specific knowledge of the industry helps mitigate some of the risk by reducing the level of uncertainty related to industry (Baum & Silverman, 2004). Industry experience increases the founder's exposure to current trends in the industry and reduces technology uncertainty (Delmar & Shane, 2006).

Prior startup experience
Building a product or service and taking it to the market requires a different level of knowledge set that cannot be gained by working in an industry. The founder needs to overcome uncertainty by experimentation (Hsu, 2007). The knowledge gained through this effort is highly invaluable and these experiences greatly benefit the founder for future startup endeavors. Experiential learning gained through the effort of creating a venture and acquiring requisite expertise holds far more value that translates to performance of firm (Reuber, 1994). They may have raised funds for their prior startups and would have built connections with the various investors (Stuart et al., 1999).

Founder's count
Likeminded individuals who have worked as employees in large organizations found new venture firms and this leads to better cohesion of the team at least during the initial years. Due to the cohesivity, the founders engage in activities with the same view and choose the best course of action of the firm (Klepper, 2001). In fact, new venture firms with multiple founders provide signals to investors about the quality and more human capital leads to greater capital accumulations (Certo, 2003).
The abovementioned human capital factors should have the greatest influence on the first round of funding. This study posits that the signals emitted by the four human capital factors mentioned above should positively influence the amount raised in the first funding round. Therefore, H1a: Education from a premier institution positively influences the amount raised in the first round of funding.
H1b: The more the industry experience, the more the amount raised in the first round of funding.
H1c: Founders with prior startup experience can raise more funds in the first round of funding.
H1d: The more the number of the founders, the more the amount raised in the first round of funding.

Persistence of signaling over subsequent rounds of funding
Existing literature failed to analyze signaling over multiple rounds of funding leading to a gap in current literature (Baum & Silverman, 2004;Zimmerman, 2008). Funding is rather a continuous process, as firms need not just the first round of funding, but also a continuous flow of capital during their lifecycle to meet their needs for scale and growth (Colombo & Grilli, 2007). This paper analyzed startups that received the first round of funding and further examined the effects of the various signals that were pertinent and important during the first round and their validity and strength beyond the first round of funding.
The proposition is that the signaling influence from most of the human capital factors should diminish as the startup progresses on its journey and moves beyond the first round of funding to the subsequent funding rounds. The only human capital factor that should significantly affect the future rounds of funding should be the premier institute education due to two reasons. Firstly, founders graduating from premier institutions would have higher quality of education and would be more innovative in nature (Marvel & Lumpkin, 2007). The quality of education gained should help in building significantly better innovative products and processes that will provide multi-ple unfair advantages (Marvel & Lumpkin, 2007). Secondly, founders from premier institutions have strong social networks that extend within the investor communities (Miller et al., 2015). A strong social network enables required connections to future investors.
H2: The signal emanating from premier institution will persist across subsequent rounds of funding. However, the signaling effects of other human capital factors reduce in each subsequent round thereby lowering the influence on the likelihood of funding closure in each respective round.
H3: Startups with founders from premier institutions can raise more amount of funds in future rounds of funding. However, the signaling effects of other human capital factors reduce in each subsequent round thereby lowering the influence on the amount of funds raised in each respective round.

Factoring in first round funding and its influence over subsequent rounds of funding
The amount of funds raised in the first round is an important critical step for any new venture firm (Audretsch, 1995). The first round funding is an important signal for subsequent rounds of funding in two critical ways. Firstly, it provides the much-needed initial capital to test and build the new product/service. Many new ventures fail due to lack of initial support for their ideas and products (Kazanjian, 1998).
Secondly, a new venture firm is laced with great uncertainties (Hannan & Freeman, 1984) and has very few external associations. The initial round of investment provides a unique opportunity for a firm to validate the capability of its founders, solutions and prospects of the firm (Baum et al., 2000). The signaling effect of the first round funding should be very strong (Hsu, 2004;Stuart et al., 1999) and enable subsequent rounds of funding for the new venture firm.
However, later rounds of funding do not hold the same significant value compared to the first round of funding (Kazanjian, 1998;Colombo & Grilli, 2007). The amount raised in the second round of funding should not influence the amount raised in the third round of funding.
H4a: Second round funding amount is influenced by the amount raised in the first round funding.
H4b: Third round funding amount is influenced by the amount raised in the first round funding, but not by the amount raised in the second round of funding.

Funding rounds and attracting investors
Funding rounds are important determinants that signal the prospect of a new venture firm; its quality reduces the potential uncertainty that arises due to information asymmetries. The credibility associated with each funding round provides a strong signaling mechanism to other investors (Davila et al., 2003). Since information asymmetries exist during the early stages, each successive round of funding closure helps reduce the uncertainties of the new venture firm to the labor market (Gompers & Lerner, 1999) and future investors.
The factors that impact just one round of funding or at IPO are different than when looked a broad spectrum across the lifecycle of the firm (Zimmerman, 2008). Studying in a premier institution would be the distinctive variable that would provide the strongest signal to future investors for multiple reasons. First, founders who educated in premier institutions will be able to build innovative products and solutions that are capable of differentiating them from other competitors (Marvel & Lumpkin, 2007). Second, the social network effect of studying in premier institutions should help with bridging gaps and introductions to investors at venture capital firms (D'Aveni, 1990). Beckman (2006) found that founders worked within the same industry are able to build relationships to important stakeholders thereby enhancing their capability in closing more funding rounds and attracting more investors.
H5a: New venture firms can close more funding rounds if founders have completed education in a premier institution.

RESEARCH AIM
The research aim is to disentangle the signaling effects of founders' human capital factors across multiple funding rounds, prove the non-persistence nature of signals across each subsequent round of funding, and emphasize impact of funds raised in the first round of funding.

Measures
Three different analyses were carried out in this paper: 1) likelihood of receiving funding using binary logit regression; 2) robustness check using linear regression to analyze effect of human capital on funding amount raised; Effect of human capital in attracting investors and getting more funding rounds was determined by Poisson regression. Here the dependent variable is categorical -number of investors and number of funding rounds, respectively. To check whether first round funding amount influenced second round and third round funding amount, the Pearson correlation test was conducted among these variables.  Amount of funding raised in first round

H4a
Amount of funding raised in first round

H2
Probability of raising funds in additional rounds

H3
Amount of funding raised in additional rounds

Dependent variables
Funding raised -this dependent variable refers to the successful closure of funding by the new venture firm from angel investors and venture capital firms in a specific round. The likelihood of getting funding in each round is represented by the dichotomous variable and amount of funding raised in each round is represented by continuous variable with value in USD.
Funding rounds -the number of funding rounds that have been successfully closed by the new venture firm.
Investors -number of investors attracted by the new venture across all funding rounds.

Premier institution education
Founders educated from IIT and IIM, premier institutions in India were given a score of 1. It is calculated by multiplying founders having premier institution education by number of founders. Also, founders who studied abroad carried a score of 1.

Industry experience
Refers to the amount of time spent by the new venture firm founders in the e-commerce sector and internet industries. It is calculated as the number of years of industry experience of each founder multiplied by number of founders.

Prior startup experience
Refers to the total number of years of experience that the startup founders have gained at founding new venture firms before starting this new venture.

Founder's count
This variable refers to the total number of founders present in the new venture firm.

Control variables
This study controlled for industry and sector to capture the differences in the capability of firms in acquiring funds. Certain industries and sectors have variable funding needs, so it is important to control for both the industry and the sector.
The e-commerce industry/sector was chosen for several reasons. First, India has an online population of over 500 million users as of 2017 with a compound annual growth rate of 13%, which is 4 times the global growth rate. The Indian e-commerce industry has been the greatest beneficiary of this growth (Kumar et al., 2012). Back in 2010, the e-commerce industry was a mere USD 5.9 billion market, but has seen rapid growth and was over USD 36 billion in the year 2017 and poised to grow over USD 150 billion in the next 5 years. From an investor's perspective, the e-commerce industry is the most lucrative market for investments with the potential to generate multi-bagger returns. This has led to a flood of global funds and investors to the Indian e-commerce industry. This was one of the main reasons to focus on this sector.
Secondly, in a growing e-commerce market, the main aim of the new entrants is to capture the market as much as possible. Amazon and other e-commerce firms in US had followed a similar strategy during the early days of the e-commerce boom and the need for expansion and the need to capture the market pushed these firms to raise multiple rounds of funding (Kshetri, 2007). Binary logistic regression was used to analyze the effect of human capital variables on closing each funding round successfully. Three different estimations were carried out for three rounds of funding viz. startups that raised more than a.one round of funding b. two rounds of funding c. three rounds of funding.

RESULTS
Dependent variable is dichotomous, value 1 if funding round is successful, otherwise 0. The chances of receiving more than one round of funding are influenced only by founders' premier institution education and industry experience. Only premier institution education among other human capital variables influenced the outcome from more than two rounds of funding.  Table 4 depicts the odds ratio for more than one round of funding. As seen from the table, the human capital factor premier institution education has a p-value of 0.005 and an odds ratio of 1.66. Also, the p-value for industry experience is 0.028 with and odds ratio of 1.04. However, industry experience effect was seen only under unadjusted estimate.Poisson regression was used to analyze the effect on number of funding rounds. Dependent variable is a categorical variable viz. number of funding rounds. Result concluded that founder's premier institution education and their industry experience were influential in getting more funding rounds.
The p-value for premier institution education was .002 with coefficient value of .152 and p-value for industry experience was .011 with coefficient value of .013 for unadjusted estimation. Goodness-offit result proved that model fit the data.
Poisson regression was used to analyze the effect of human capital variables for attracting the number of investors. Only founders who have had premier institution education were able to attract more investors.
For premier institution education, both unadjusted and adjusted estimation p-values were less than .001 with coefficient values .224 and .214, respectively. This result was also verified through goodness-of-fit result. Table 6 shows that second round funding amount and third round funding amount correlated to first round funding amount. Second round and first round funding amount were highly correlated with Pearson correlation val- Note: * p < .05, ** p < .01, *** p < .001.  Investor's count = 2

Robustness checks
In order to run robustness checks, multiple analysis of the data set to assess validity of results and rule out alternative explanations and mechanisms were carried out. Linear regression analysis on each round of funding was done to assess the impact of the human capi-tal factors on the amount of funding received by the startups. This gave an estimate of the impact of only startups that were successful in closing the round of funding. The results of the linear regression across each round of funding are listed in Table 7. Dependent variable was transformed using natural log. Effect of human capital variables on the amount of first round funding result showed that premier institution education, industry experience and prior startup experience of founding team had influence on the amount raised in the first round funding.   Among these three variables, the premier institution education had highest estimate of .438. The model fit well and it is shown in residual vs fitted plot ( Figure 6).
For the second round, only premier institution education had significant impact on the second round funding amount raised. None of the other human capital variables had any influence. In third round of funding, the human capital variables had no effect on the amount of funding raised.

Discussion
The findings contribute to the existing literature by providing a novel view on the signaling effects of the human capital factors across multiple rounds of funding. The results are summarized below.
Hypotheses 1a, 1b, 1c were supported. The significant predictors for raising first round amount were premier institution education However, the significance reduced for each funding round. Other predictors were not significant.
Hypothesis 3 was supported. Founders with premier institution education raised more amounts of funding and the significance reduced in each subsequent round of funding. It was observed that premier institution education was highly signifi-  and had no significance in the third round of funding. This demonstrates the diminishing effect of the premier institution education across each subsequent round of funding. As stated in Hypothesis 1, the industry experience and prior startup experience were significant only during the first round of funding, but had no significance in the second and third round of funding illustrating the diminishing value of these variables.
Hypotheses 4a and 4b were supported. The amount raised in the second round of funding was highly influenced by the amount raised in the first round of funding with Pearson coefficient = 0.749 and p-value < 0.001. The amount raised in the third round of funding was also influenced by the amount raised in the first round of funding, but with less effect. Pearson coefficient was 0.455, p = 0.033. The amount raised in the third round of funding was not influenced by the amount raised in the second round of funding.
Hypotheses 5a and 5b were supported. Premier institution education attracted more number of funding rounds with The findings have important practical and research implications for practitioners and academics in the area of funding acquisition for new venture firms.

Implications for practitioners
This study analyzed the effect of the first round of funding and its correlation to future rounds of funding. This study found that the amount raised in first round was highly correlated to the amounts raised in the second round of funding and the third round of funding. Investors provide an overarching importance to the amount of funds raised in the first round. The higher the amount of funds raised by a new venture firm in the first round, higher the likelihood for the firm to raise larger amounts of funds in the subsequent rounds. This provides an interesting proposition for practitioners viz. the new venture firms; they should attempt to raise a large amount of funding in their first round irrespective of the need for the large amount as this positively influences future rounds of funding.
This study also concluded that new venture firm founders should team with other founders that have Table 8. Linkage of proposed method and results

Result steps
Influencing factors Link to proposed method Table 3. Estimates examining more than one round, two rounds and three rounds of funding using binary logit regression Likelihood of receiving more than one, two and three rounds of funding is influenced only by premier institution education. Adjusted estimate of premier institution education = 0.412** for more than one round, 0.396* for more than two rounds, 0.512* for more than three rounds of funding Hypothesis 2 Table 5. Estimates for number of funding rounds and number of investors attracted using Poisson regression Premier institution education and industry experience attracts more number of funding rounds (5a, 5b). Adjusted estimate for premier institution education = 0.152**, industry experience = 0.013*. Premier institution education attracts more number of investors (6a): adjusted estimate = 0.224*** Hypotheses 5a, 5b, 6a Table 6. Relationships between amounts raised in each funding round Hypotheses 4a: second round funding amount is influenced by the amount raised in the first round funding. Pearson correlation = 0.75*** Hypotheses 4a, 4b Hypothesis 4b: third round funding amount is influenced by the amount raised in the first round funding, but not by the amount raised in the second round of funding. Pearson correlation = 0.46* Table 7. Estimates for amount of funds raised across multiple rounds of funding using linear regression Adjusted estimate for premier, industry and prior startup experience = 0.420***, 0.032*, 0.104*** Hypotheses 1a, 1b, 1c Adjusted estimate for premier institution education = 0.565* for second round of funding Hypothesis 3 education from premier institutions. Investors are attracted and provide funding to venture firms with founders from premier institutions. Impact of the premier institute is understandable and justified as founders from premier institutins could build products and services that create a distinct advantage over existing products and services and have more innovation radicalness.

Implications for researchers
This paper disentangled the effects of various human capital factors across three funding rounds as opposed to existing literature that focused only one stage first funding round or IPO stage. It proved varying impact of each of the human capital factors at different stages of the startup and gradual diminishing effect followed by zero effect as the startup raised each consecutive round of funding. This echoed with Hoenen et al. (2014) who studied the impact of patents across early rounds of funding. The only factor that continued to have sustained impact across the three rounds of funding was the education from premier institutions.
Secondly, previous research only focused on the education level completed by the founders, but did not look at the quality of the education as determined by the education completed from a premier institution (Klotz et al., 2014). This was a substantial finding of the research, which helped unravel the unique dimension of quality of education and its impact on new venture firms. This study found that the premier institution human capital factor was the unique and the only factor that sustained across the first three rounds of funding.

Limitations and future research
To conclude, this study has limitations that open the scope for further research. First, distinguish equity based funding from debt based funding of startups as the characteristics of the funds vary between the two. Second, look at the impact of patents and its relation to each funding round for startups with founders from premier institutions. Third, distinguish types of investors, i.e. distinction among angel investors, early stage venture firms and mid-stage venture firms and study the signaling effect from each of them separately.

CONCLUSION
The early rounds of funding for new venture firms are fraught with complexities and uncertainties. This study has disentangled the signaling effects of the human capital factors across three funding rounds and proved the diminishing value of signals across each subsequent round of funding as investors look for other cues to make a funding decision. This would be a new contribution to the study of signaling and human capital theory. This study unraveled the importance of quality of education and the significant importance given to it by investors across multiple rounds of funding. Founders graduating from premier educational institutions were able to attract more investors and close more funding rounds. It also highlighted the importance of the amount raised in the first round of funding and the leverage it provided to future rounds of funding. Irrespective of the need for larger amounts, founders need to strive and raise more funds in their first round. Overall, the findings demonstrated the various funding acquisitions drivers and their diminishing impact during the early stages of funding for new venture firms.