Business angels and informal investing : a look at the underlying decision making process

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Business angels and informal investing: a look at the

underlying decision making process

Denislav Denchev (10224254) Supervisor: Joeri Sol

28-06-2015

Faculty of Economics and Business BSc Business Administration

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Statement of originality

This document is written by Denislav Denchev who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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1. Introduction

One of the most important goals during a startup’s early days is securing funding. This period is usually associated with intensive research and new products development, performed however under low resource efficiencies, due to the inability to exploit economies of scale and shared resources (Baum et al., 2000). In the period after the Great Financial Crisis, a high number of people lost their jobs and therefore decided to start their own enterprises. This, combined with the strict financing rules introduced by the major banks, made the competition for the scarce capital on the market even fiercer.

In such an economic environment, relatives, crowd funding and angel investors became the last hope for a number of entrepreneurs lacking the necessary capital to develop their ideas. In his paper of 2007, Sohl suggests that business angels (BAs) represent a significant part of the provided financing to startups and small ventures. Still, while there are many angel investors currently active in North America (Riding, 2008), reliable data collection about their activities is highly challenging, hence the lack of extensive research on the topic.

Since securing capital can be the difference between survival and extinction during a business’ emerging phase, knowing the factors influencing BAs investment decisions is increasingly valuable for prospective entrepreneurs. Therefore, the main goal of this thesis research will be to examine the decision making mechanisms utilized by angel investors and explain their effect on entrepreneurs’ success.

To that effect, this paper compiles information from 116 interactions between

entrepreneurs and business angels, part of the American TV show “Shark Tank”. In the next step, eight objective criteria, adopted from Maxwell et al. (2011), are used to analyze the obtained data. Those are complemented by Presentation Quality and Success measures, introduced exclusively in this study. Finally, a set of regressions are run in order to test the effects of the selected factors on receiving a financing offer and the success of entrepreneurs in the show.

The initial results confirm the findings of Maxwell et al. (2011) that venture capitalists rely on elimination by aspects to select a business opportunity. However, after testing their robustness, it is uncovered that the actual decision making process BAs employ is differing from the one described in previous studies. The current body of knowledge is further expanded by presenting the effects of Presentation Quality and introducing an objective measure of success.

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This paper follows the subsequent structure. Section 2 introduces the readers to the existing literature and its limitations. Section 3 describes the detailed methodology utilized in this paper. Section 4 presents and discusses in full the results of this study. Section 5 outlines the conclusion and suggests ideas for future research on the topic.

2. Previous research and its limitations

Adopted as a base for this paper is the study performed by Maxwell et al. (2011). In it the authors collect data from interactions between entrepreneurs and potential investors, with the goal of examining early stage decision making. Eight key criteria are selected and evaluated by independent raters. Afterwards, the effects of the critically low scores and the average score per project are analyzed.

An important distinction is that Maxwell et al. (2011) break down the investment process in phases and specifically focus on the selection stage, during which the information exchanged between entrepreneurs and investors is mainly objective.

The results of Maxwell et al. (2011) are in contradiction to the majority of past research, by suggesting that BAs rely solely on elimination by aspect heuristic to form a decision during the selection stage. Furthermore, the authors argue that after this selection stage is passed, the decision making process varies with every individual.

Even though the majority of academics have been focused mainly on the factors that allow startups to pass through the initial selection stage, in my opinion, this is not an accurate representation of success. Albeit a big part of the ventures that fail to receive funding stop existing, receiving an offer that falls below the financial needs of a venture, cannot be defined as fully successful either. Following the direction proposed by Hall and Hofer (1993), this research will focus on the interaction between entrepreneurs and BAs. The focus will be put on the moment of its occurrence, eliminating the confirmation bias, associated with asking investors to recall their previous behavior.

At the moment of decision-making, the information available for investors will likely exceed their cognitive capacity to analyze it. Therefore, Mason and Stark (2004) argue that while angel investors will evaluate the business opportunity in front of them based on objective factors, such as sales numbers and individuals’ expertise, a high score on those parameters will likely be insufficient to secure an investment. Tversky (1972) proposes the theory, that in such situations

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BAs will abandon the compensatory model (in which high scoring on a factor can compensate for a low score on another one) and will depend on some type of decision-making heuristic, sacrificing part of the accuracy for speed of judgment, which is crucial in real life. He considers elimination by aspects (EBA), where investors reject ideas that show signs of fatal flaws, to be a good reflection of the real world selection process. While this method is in contradiction with the rationale that a decision maker should use all relevant information in his/her selection process, it is in line with the idea that the evaluated attributes will be ordered starting from the most

relevant one.

This research will adopt the EBA factors considered by Maxwell et al. (2011), however in the same time it will add two additional ones. The first one concerns the quality of the business presentation pitch. In their research, Sparks and Areni (2002) argue that presentation quality can have a positive or a negative impact on the audience’s attitude towards the subject. This underlines the importance of the excellence with which candidates present their ventures and the influence it may have on the BAs, given the time constraint under which they operate.

While the study performed by Maxwell et al. (2011) focuses on the initial selection process adopted by BAs, this paper will extend the scope by evaluating the overall success of participants in the show. This new dimension takes into account the assumption that an

entrepreneur who accepts an offer exceeding his/her initial company valuation is more successful that someone who settles for a lower bid. Furthermore, the “Shark Tank” has a rule that a

candidate needs to secure the full amount that he or she requested, in order to receive funding. Therefore, determining the right venture value is an important strategic decision, since it also defines the starting point for negotiations with the “Sharks”. Additionally, if the requested amount is exceptionally high, the BAs may consider it as a negative sign about the owner’s ability to estimate the actual value of its business. Respectively, if the proposed valuation is too low, it may signal a lack of belief in the business idea or low commitment from the

entrepreneur’s side.

Following the debate in the existing literature, the focus of this paper will be to understand the factors influencing the interaction between investors and entrepreneurs, specifically the decision-making process that BAs follow when choosing their investments.

In the spirit of the findings presented by Maxwell et al. (2011), Hypothesis 1 suggests that Critical Flaws observed during the business idea presentation will predict the BAs’

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Rejection to provide funding. Previous research suggests that angel investors depend on elimination by aspects heuristics in order to decide whether to provide a funding offer. This expectation will be put to test using the obtained data from the 5th season of “Shark Tank”.

Hypothesis 2 predicts that Success is not affected by the number of Critical Flaws and the Average Rating of the project. In this paper, Success encompasses two metrics. First of all, for entrepreneurs to be successful, they need to receive and accept a funding offer from the

“Sharks”. This definition is further extended by the assumption that a participant who has

secured funding exceeding his/her proposed company valuation is more successful than someone who has accepted an offer below his/her initial expectations.

Hypothesis 3 introduces an additional variable, not considered in the study of Maxwell et al. (2011), namely Presentation Quality, and suggests it will have a positive effect on receiving a financing offer. In their research, Sparks and Areni (2002) underline the relationship between Presentation Quality and persuasion. This is used as the base for this hypothesis, triggering the expectation that entrepreneurs who present their business idea well, have a higher chance of receiving an offer, than those with lower presentation skills.

Finally, this paper builds further on the Presentation Quality variable and examines its relationship to the size of the accepted bids. Therefore, Hypothesis 4 is defined as follows: Presentation Quality has a positive effect on Success.

3. Research Method

“Shark Tank” is an American reality TV show, premiered in 2009 and currently airing its sixth season. The format consists of entrepreneurs presenting their business ideas in front of a board of 5 investors called “Sharks”, which are looking for a direct equity in the startups. The board consists of Kevin O'Leary (venture capitalist), Barbara Corcoran (real estate mogul), Robert Herjavec (founder and CEO of Herjavec Group – IT security provider), Lori Greiner

(telemarketing specialist and investor) and Mark Cuban (tech entrepreneur and owner of NBA's Dallas Mavericks). Since all members have a different business background, they represent different commercial areas with their expertise.

When facing the “Sharks”, each participant states his or her name and gives a short introduction of the presented business, alongside with the amount requested and the proposed equity stake in return. Afterwards, the board of investors asks questions in order to obtain a

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better understanding of the business characteristics and potential profitability, as well as test the knowledge and managerial expertise of the candidate. The interaction continues until either all 5 “Sharks” withdraw, giving a short explanation, or one or more of them makes an offer. This is followed by a short round of negotiations, after which the candidate can accept or reject the final offer. An important rule of the “Shark Tank” is that the candidates need to secure the full amount they ask for, or they will receive nothing.

To collect data, the fifth season of the show is used, since it is the most recent one which is already fully aired. It was shown form 20th_{of September 2013 to 16}th_{of May 2014 and} consists of 29 episodes of 42 minutes each, presenting a total of 116 business ideas. An

observational interaction is used, as presented by Maxwell et al. (2011), which allows a rater to extract interaction patterns and interpret behaviors form a videotaped material.

Each investment opportunity was assessed based on the 10 critical factors described below. The adopted scoring format is similar to this of a credit rating agency, where A-grade (AAA, AA, A) denotes a positive score on a specific factor, B-grade (BBB, BB, B) a neutral score and C-grade (CCC, CC, C) indicates a critical flaw. Furthermore, all ratings are translated into numerical values, to facilitate further analysis, based on the following scheme: AAA=10, AA=9, A=8, BBB=6, BB=5, B=4, CCC=2, CC=1 and C=0. The missing scores of 7 and 3

exaggerate the gap between the positive, neutral and negative scores, highlighting its importance. Consequently, the additional variable Average Project Rating was introduced, by calculating the average numerical score on the 8 critical factors for each project.

This paper further aims to expand the existing body of knowledge, by incorporating a Success variable in the analysis. Success is defined by accepting an offer and the deviation of the proposed value from the total company valuation, presented by the entrepreneur. This means that three different entrepreneurs, valuing their entire companies at $1 000 000 each, will receive a Success score of -50%, 0% and 50% if they receive a total valuation from the BAs of $500 000, $1 000 000 and $1 500 000 respectively. Consequently, the higher the percentage score, the higher the level of success.

All criteria, with the exception of the Presentation Quality, follow the requirements of being objective, diagnostic (being positively correlated with a venture’s success) and easy to observe. Regardless of the subjectivity of the Presentation Quality factor, it is considered to be a key determinant of a candidates’ success in the “Shark Tank”, therefore it needs to be included

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for further analysis. A full description of all evaluation factors is provided in Table 1 of the Appendix.

3.1.

Validity concerns

The main validity concern is based on the fact that a TV show is the source for the data

collection. This raises questions about the generalizability of this “artificial environment” data for real world situations. Nonetheless, multiple academic studies (e.g. Hartley et al., 2005; Levitt, 2004; and Maxwell et al., 2011) have previously used naturally occurring data from TV shows like “Who Wants to Be a Millionaire”, “Weakest Link” and “Dragon’s Den”, and confirmed that it is generally applicable to real world circumstances. Furthermore, the Business Angels in “Shark Tank” are using their own capital to fund the initiatives they select and have sufficient time to ask questions and take a decision, additionally adding to the real life comparability. In general, “Shark Tank” provides a unique opportunity to analyze a considerable amount of data, collected under the same conditions. This method is more reliable compared to previous research which depended on BAs to personally evaluate and explain their past behavior (Wiltbank et al., 2009).

An additional factor, that may potentially bias the results, is the fact that the scores on the 8 critical criteria are provided only by the author of this paper. Due to the short timeframe in which this research was performed, it was not possible to collect data from multiple raters in order to increase the inter-rater reliability. Nonetheless, in order to overcome this drawback, the established criteria used for evaluation of all projects (Table 1), were defined before the data collection has started. Furthermore, a short description representing each score was developed, with the sole purpose of keeping the grading as objective and undisposed to bias as possible.

4. Results

Existing research on the topic presents two contradicting views. Payne et al. (1993), suggests that business angels take into account a long list of factors, combined into an evaluation model, when forming their decision to finance a business idea. On the other hand, Maxwell et al. (2011) expose drawbacks in the previous research and conclude, in their article, that the

non-compensatory decision making model of elimination by aspects represents the decision making heuristics of angel investors better.

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Based on the analyzed data, my research supports the findings on Maxwell et al. (2011), that EBA is a method utilized by business angles, due to its efficiency in an environment where large quantity of business ideas have to be assessed. In the same time, the data suggests, that when BAs do not reject a candidate, lack of critical flaws is not a sufficient prediction that the candidate will receive an offer in line with his/her proposed total company valuation. A descriptive statistics summary of the collected data is provided below.

Table 2. Descriptive Statistics and Correlations between variables.

Mean

Std.

Deviation N 1 2 3 4 5 6 7

1. Number of Critical Flaws 1.43 1.60 116 1.00

2. Average Project Rating 5.85 1.48 116 -.86 1.00

3. Presentation Quality 6.22 1.06 116 -.29 .32 1.00

4. Presentation Quality (Outliers Removed) 6.38 0.85 63 -.16 .22 1.00 1.00

5. Presentation Quality vs. Avg. 0.42 0.50 65 -.16 .26 .79 .84 1.00

6. Rejection Binary 0.35 0.48 116 .56 -.57 -.20 - - 1.00

7. Deviation Percentage -0.34 0.40 65 -.17 .34 -.21 -.02 -.07 - 1.00

Furthermore, Table 3 outlines the number of projects, with and without Critical Flaws, that received a funding offer, as well as those which were rejected. It is interesting to see that 38, or almost half of the businesses which had a critical flaw received an offer from the “Sharks”. Those findings are in contrast to the results of Maxwell et al. (2011), who suggest that a Critical Flaw eliminates the opportunity to receive funding. Nonetheless, further examination shows that out of the 39 ventures which lacked Critical Flaws, only 2 were rejected. Those figures suggest that a relationship between the Number of Critical Flaws and receiving an offer exists. Therefore, this claim will be statistically tested in the next section.

Table 3. Number of Rejections and Offers received.

Received an Offer Rejected

Projects with Critical Flaw(s) 38 39

Projects without Critical Flaw(s) 37 2

4.1. Hypothesis 1 (Critical Flaws Predict Rejection)

After analyzing all 29 episodes of the fifth season of “Shark Tank”, in accordance to the rules explained into the Research Method section, some variables were given numerical values in

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order to facilitate further analysis. For the purpose of defining the Rejection variable, a value of 0 was assigned to all projects which received an offer from the “Sharks” and a value of 1 to those which were rejected. Furthermore, the number of critical flaws was calculated for each of the 116 projects and then a linear regression was performed in order to test the suggested hypothesis. The results in Table 4 propose a strong relationship between the number Critical Flaws and the BAs decision to reject a candidate, shown by the R2 of 0.312. This relationship proves to be statistically significant, supported by the p-value lower than 0.01.

Table 4. Results Hypothesis 1 test.

Rejection Binary (DV)

Model 1 Model 2 Model 3

Coefficient SE Beta Coefficient SE Beta Coefficient SE Beta

Constant .114** .050 .897*** .337 .936** .378

Number of Critical Flaws .168*** .023 .558*** .079* .044 .262* .078* .044 .261*

Average Project Rating -.112** .048 -.346** -.111** .048 -.342**

Presentation Quality -.008 .037 -.017

R sqrt. .312 .344 .344

Note: Dependent variable is Rejection Binary. N=116. *p<0.1. **p<0.05. ***p<0.01.

Furthermore, models 2 and 3 were introduced by performing a robustness check, including Average Project Rating (APR) and Presentation Quality. This was done with the purpose of determining if the Number of Critical Flaws remains a significant explanatory variable for Rejection.

The results suggest, that including APR in Model 2, slightly increases its predictive power to 0.344. However, the Number of Critical Flaws variable becomes only marginally significant at the 10% level. On the other hand Average Project Rating has a p-value lower than 0.05, suggesting that it is also a valid predictor of Rejection. Its negative beta value suggests that if APR increases with one standard deviation, the Rejection probability will decline with 0.346 standard deviations. Moreover, Model 3 examines also the effect of Presentation Quality. Though, this doesn’t affect the R2_{of 0.344.}

These findings suggest that business angels engage in a selection process by utilizing also some kind of compensatory model, alongside EBA. While the Number of Critical Flaws variable still has some predictive power, it is being complemented by the APR. This observation is in contrast to the findings presented by Maxwell et al. (2011), who suggest that informal investors

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rely solely on elimination by aspects to decide if to fund a venture. Another potential

explanation, may lay in the decision of the authors to split the selection process in an initial and due diligence phases. Since this paper considers the selection procedure as a whole, thus

differing in methodology from the existing articles, such deviations can be expected. In order to provide a concrete conclusion on this point, more detailed examination on the differences between the selection stages definitions is needed.

4.2. Hypothesis 2 (Lack of Critical Flaws and Average Project Rating don’t predict Success)

Hypothesis 2 was analyzed using a sample of 55 projects which were financed through the show. After performing a regression on the effect of Number of Critical Flaws and Average Project Rating on the Success score, the Hypothesis has been rejected with a p<0.05. Additionally, the observed relationship between the variables is moderate as suggested by the R2 score of 0.136 (Table 5).

Deviation Percentage (DV)

Model 1

Coefficient SE Beta

Constant -1.416*** .443

Number of Critical Flaws .072 .079 .151

Average Project Rating .116*** .062 .444***

R sqrt. .136

Note: Dependent variable is Deviation Percentage. N=55. *p<0.1. **p<0.05. ***p<0.01.

However, by creating scatter plots and examining the model closer, it can be seen that only APR is a statistically significant predictor of Success. Moreover, by itself, it explains 12.3% of the variance in the dependent variable (Graphs 1 and 2 of the Appendix). Therefore, it can be concluded that while the Average Project Rating is affecting the Success of “Shark Tank” participants, the Number of Critical Flaws is not related to the company valuation they receive.

The fact that APR has a direct influence on Success is opposing the results shown in previous research. Even though the Success variable was specifically introduced in this paper,

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Maxwell et al. (2011), claim that the Average Project Rating is in no way related to the success of the participants.

While my findings make an opposing statement, this and other deviations from the existing body of knowledge may be due to the different countries from which the data was collected.

4.3.

Hypothesis 3 (Presentation Quality predicts receiving an Offer)

As already discussed, previous research suggests that Presentation Quality has a positive effect on persuasion (Sparks & Areni, 2002). Additionally, Graph 3 of the Appendix outlines the number of offers and rejections for each Presentation Quality score. Building on the existing literature and Graph 3, my expectation is that entrepreneurs who do well in presenting their business, will have a higher chance of receiving an offer. In order to test that, the Rejection variable was regressed against the Presentation Quality, to identify the relationship between the two.

Rejection Binary (DV)

Constant .921*** .262 .241 .244 .936** .387

Presentation Quality -.091** .041 -.202** -.020 .037 -.043 -.008 .037 -.017

Number of Critical Flaws .164*** .024 .546*** .078* .044 .261*

Average Project Rating -.111** .048 -.342**

R sqrt. .041 .313 .344

Note: Dependent variable is Rejection Binary. N=116. *p<0.1. **p<0.05. ***p<0.01.

The resulting R2 of 0.041, shows that the proposed Model 1 explains only a small portion of the variance in the dependent variable, however the accompanying p<0.05, proves the

statistical significance of the results. This regression outcome, showing that higher Presentation Quality results in lower rejection rate, allows to confirm H3. Nonetheless, as a next step,

robustness tests including Number of Critical Flaws and APR were performed, in order to test the consistency of the results. After the insertion of Number of Critical Flaws in Model 2, it became the only statistically significant predictor of the dependent variable and increased R2 considerably to 0.313. Model 3 for this hypothesis is identical with the one presented in H1 and encompasses Average Project Rating, as well. By raising the predictive power of the model to

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0.344, APR became the only variable with p-value lower than 0.05. Number of Critical Flaws retains only marginal significance at the 10% level.

4.4.

Hypothesis 4 (Presentation Quality predicts Success)

My final expectation, outlined in Hypothesis 4, is that the quality of the presentation given by an entrepreneur will have a positive affect not only on receiving a funding offer from the business angels, but also on the monetary size of the offer. To test this prediction, the Deviation

Percentage of the companies’ value, introduced in Hypothesis 2, was regressed against the Presentation Quality variable.

Constant .191 .322 .363 .331 -.717 .479

Presentation Quality -.084* .050 -.207* -.100** .050 -.248** -.121** .048 -.299**

Number of Critical Flaws -.098* .055 -.219* .031 .068 .069

Average Project Rating .172*** .058 .454***

R sqrt. .043 .089 .204

This analysis resulted in a low R2 of 0.043 and a marginally significant predictive variable. However the findings suggested that higher Presentation Quality results in lower

financing offer from the investors, therefore rejecting H4 (Graph 4). Following the rationale used in the tests of Hypothesis 1 and 3, two additional models were introduced in order to determine the robustness of Presentation Quality as a predictor of the dependent variable. Model 2 includes Number of Critical Flaws as well. While the R2 more than doubles to 0.089, the predictive power of the model remains low. Even though, the p-value for Presentation Quality became lower than 0.05, the full model is still statistically significant on the 10% level. On the other hand Model 3, showcases a strong predictive power for the Deviation Percentage (R2=0.204), by introducing Average Project Rating as an independent variable. More importantly, the model’s p-value drops to less than 0.01, underlining its statistical significance.

After a detailed examination of Graph 4 (representing Model 1), it was uncovered that two observations were outliers in the dataset, with Presentation Quality scores of 3 and 9.

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Therefore, in order to increase the accuracy of the model, those observations were removed and the regression was rerun once again.

Table 8. Results Hypothesis 4 test (Outliers Removed).

Model 1

Constant -.314 .359

Presentation Quality (Outliers Removed) -.008 .056 -.017

R sqrt. .000

The results of the new regression are considerably different, with the model explaining less than 1% of the variance in the Deviation Percentage variable (R2 = 0.0003) and losing its statistical significance (p-value = 0.892).

Following these conflicting results, it was clear that further tests need to be performed. By analyzing the existing data, it was determined that the average Presentation Quality score is 6. As a next step, a new variable (Presentation Quality vs. Avg.) was created and given a value of 0 for Presentation Quality scores of 6 and below, and 1 for Presentation Quality scores higher than 6. This splits the observations in below and above average. Furthermore, the effect of this new variable on the Deviation Percentage was tested.

Table 9. Results Hypothesis 4 test (Presentation Quality vs. Avg.).

Model 1

Constant -.321*** .066

Presentation Quality vs. Avg. -.055 .102 -.068

R sqrt. .005

The results uncover a weak (R2 = 0.005), statistically insignificant (p-value = 0.593) relationship between the two variables, therefore suggesting that Presentation Quality doesn’t

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have a positive effect on the company value, proposed by the investors. As a consequence, Hypothesis 4 was rejected.

5. Discussion and Conclusion

This paper addresses the contradicting results of the existing research on BAs’ decision making. Furthermore, it contributes to the current body of knowledge, by examining the effects of Presentation Quality, as well as the key determinants of Success in the interactions between entrepreneurs and angel investors.

In contrast to the expectation, Hypothesis 1 was in line with some of the existing theory on the topic but contradicting the findings of Maxwell et al. (2011), whose paper was used as a base for this research. The performed analysis suggests that business angels depend on

elimination by aspects, as a decision making heuristic, allowing them to filter through the candidates. Nonetheless, the consequent robustness test discovered that venture capitalists use both EBA and some kind of compensatory model in their selection process.

Furthermore, the rejection of Hypothesis 2 implies that having an all rounded investment proposal is highly important, since it has a direct moderate effect on the amount investors will offer. The observed effect of APR on Success is also contrary to the previous knowledge on the topic. Nonetheless, this is the first study that specifically looks at the monetary size of the proposed valuations and their deviations from the ones requested by the entrepreneurs. This information can be exceptionally valuable for participants in TV shows of the same format. Potentially, even those who are aiming to obtain funding from individual investors in different settings may benefit from the findings of this study. Nonetheless, further research is needed to confirm the validity of the observed results under other circumstances.

Hypothesis 3 and 4 were newly introduced in this paper, stemming from the findings of Sparks & Areni (2002), who explored in greater detail the effect Presentation Quality has on persuasion. In line with my expectation, Hypothesis 3 was confirmed, though barely. Still, when testing its robustness, Presentation Quality quickly lost its significance as a predictor of receiving an offer. Those results imply, that individuals with extensive experience in venture capitalism, such as the “Sharks” in the show, are looking beyond the obvious for objective indicators of a business’ potential. This suggests, that while showcasing advanced presentation skills may be

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beneficial, it doesn’t guarantee receiving a financing offer, when a venture is underperforming on the other objective criteria.

Furthermore, the outcome of the study proposed that high Presentation Quality is insufficient to promote the offer generosity, thus rejecting H4. Those results stayed consistent also when removing the outliers from the dataset, as well as when the Presentation Quality scores were classified as below and above average. This further strengthens the proposed theory that the “Sharks” in the show are focusing more on the objective characteristics of a business.

The observed results build on the existing knowledge on the topic and further expands it by evaluating the effects of subjective factors, such as Presentation Quality. The focus is also expanded beyond the initial stage of decision making, as defined by Maxwell et al. (2011), by taking a specific interest in the financing offers that investors make and their deviation from the proposed company value. The compiled findings have clear practical implications for the entrepreneurs looking to secure funding for their business ventures. Knowing which critical factors BAs use in their decision making process, can prove helpful when designing a sales pitch, allowing to select the product aspects on which to emphasize. Eliminating critical flaws from the business, will give the entrepreneurs a higher chance of receiving a financing proposal. This however, will not be enough to ensure that the received offer will match the proposed valuation of the company. Such valuation will be more affected by the average ratings the project receives.

My findings further underline the importance of well-structured business presentation and highlight its effect on the business angels’ perception of the venture. Nevertheless, a good

presentation by itself will not be sufficient to ensure an offer exceeding the proposed company valuation.

6. Limitations and Future Research

All in all, this paper provides a glimpse on the underlying decision making of angel investors. While the results show that high ratings on some evaluated parameters of the business can have a positive effect on the received offer, the decision making process seems to be complex and accounting for multiple factors, while also ensuring the efficiency of the selection process.

The purpose of this paper was to replicate the study conducted by Maxwell et al. (2011) and extend it in the same time, by testing the effects of presentation quality. This approach however, kept the main limitation of the previous research, namely the fact that it is performed

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based on data collected from a TV show. While being an artificial environment, I believe that it provides an accurate representation of reality and there are multiple studies that support this view (e.g. Hartley et al., 2005; Levitt, 2004; and Maxwell et al., 2011). Furthermore, the “Sharks” use their own money to finance the selected business ventures, which further increases the validity of the results. The existing theory on the topic however, will benefit from testing the cross-cultural reliability of the attained results, by replicating the described procedures on a samples obtained from different countries.

This paper has shed light on the factors predicting that a business will receive a financing offer. However, no definitive evidence was obtained for the characteristics which will determine the success of an entrepreneur pitching for funding in the “Shark Tank”. Future research can focus on the specific factors which influence the way investors assign monetary value on a company. Potential variables to be examined can be the size of the existing sales, market potential and the venture risk, as constantly referred to by the “Sharks”. Yet more detailed examination is needed to uncover any underlying relationships.

Out of the 116 observations, there were 14 instances when an offer was made but the entrepreneur rejected it and went home without any funding. Further insights can be obtained by understanding if the offered company valuation was the only reason for rejecting the received bids. Nonetheless, the insufficient number of observations didn’t provide a large enough sample to test this hypothesis.

Finally, future research can focus on the further development of businesses, once they have been presented in the “Shark Tank”. During the show multiple references have been made to the so called “Shark Tank effect”, representing the publicity entrepreneurs and their ventures receive by being featured on the show. Numerous success stories were shown, of companies which either received or left the “Tank” without an offer, yet outperformed the public’s expectations due to the increased attention received after the show. It will be interesting to understand if the “Shark Tank effect” is a factor taken into account by the entrepreneurs, e.g. allowing them to take a lower than expected offer, knowing that they will be overcompensated in the future by the increased sales figures.

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7. Bibliography

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Hartley, R., Lanot, G., & Walker, I. (2005).Who really wants to be a millionaire: estimates of risk aversion from game show data. Warwick Economic Research Papers 719.

Levitt, S.D. (2004). Testing theories of discrimination: evidence from weakest link. Journal of Law and Economics 47(2), 431-452.

Mason, C., & Stark, M. (2004). What do investors look for in a business plan? International Small Business Journal 22(3), 227-248.

Maxwell, A. L., Jeffrey, S. A., & Lévesque, M. (2011), Business angel early stage decision making, Journal of Business Venturing 26(2), 212-225.

Payne, J.W., Bettman, J.R., & Johnson, E.J. (1993). The Adaptive Decision Maker. Cambridge University Press, New York.

Riding, A. (2008). Business angels and love money investors: segments of the informal market for risk capital. Venture Capital 10(4), 355-369.

Sohl, J.E. (2007). The organization of the informal venture capital market. Handbook of Research on Venture Capital, 347-370.

Sparks, J., & Areni, C. (2002). The effects of sales presentation quality and initial perceptions on persuasion: a multiple role perspective. Journal of Business Research 55, 517-528. Tversky, A. (1972). Elimination by aspects: a theory of choice. Psychological Review 79(4),

281-299.

Wiltbank, R., Read, S., Dew, N., & Sarasvarthy, S. (2009). Prediction and control under

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8. Appendix

Table 1 (Adapted from Maxwell et al., 2011).

Factor Key question Ratings Explanation

Adoption Will target consumers easily adopt the product?

AAA/AA/A Customers will easily adopt the product or service BBB/BB/B Benefits harder to identify, some adoption issues CCC/CC/C No clear benefits or major adoption issues

Product status

Is product ready for market, or is still work required before it ships?

AAA/AA/A Finished product

BBB/BB/B Design complete, all technical issues addressed CCC/CC/C Needs more research and development

Protectability

How easy will it be for

competitors to copy the product or service?

AAA/AA/A Product patented or significant other barrier BBB/BB/B It will not be easy to replicate

CCC/CC/C Anyone could copy it easily

Customer engagement

Are the first customers identified? Does the product meet their needs?

AAA/AA/A Customers in place or committed to purchasing BBB/BB/B Customers engaged in development project CCC/CC/C No customers identified

Route to market

Is there a realistic marketing plan and route to market?

AAA/AA/A Realistic marketing plan / distribution partner BBB/BB/B Options identified, but no agreements in place CCC/CC/C Limited thought given to distribution issues

Market potential

Is there a large market for the product?

AAA/AA/A Large market potential (over $20 million) BBB/BB/B Medium market potential (over $2 million) CCC/CC/C Small market potential (less than $2 million)

Relevant experience

Does senior management have direct and relevant experience?

AAA/AA/A Significant relevant experience

BBB/BB/B Limited experience, but appropriate knowledge CCC/CC/C No evidence of required experience

Financial model

Will investors in the company make money? Is the requested capital sufficient?

AAA/AA/A Sound business model and cash management BBB/BB/B Unclear profitability, limited cash management CCC/CC/C No evidence of profit or cash management

Presentation quality

Was the entrepreneur's

presentation engaging and did it address all investor concerns?

1-10

Deviation percentage

How much does the proposed company value differ from the one requested by the participant?

deal value − requested value requested value

(21)

Graph 1. Number of Critical Flaws as a predictor of Deviation Percentage.

(22)

Graph 3. Number of Offers and Rejections based on the Presentation Quality score.

Graph 4. Presentation Quality as a predictor of Success.

0 0 1 1 9 32 23 8 1 0 0 0 2 ₁ 7 22 7 1 1 0 0 5 10 15 20 25 30 35 1 2 3 4 5 6 7 8 9 10

Business angels and informal investing : a look at the underlying decision making process