Do Disruptive Visions Pay Off? The Impact of Disruptive Entrepreneurial Visions on Venture Funding

Entrepreneurs often articulate a vision for their venture that purports to fundamentally change, disturb, or re&#8208;order the ways in which organizations, markets, and ecosystems operate. We call these visions disruptivevisions. Neglected in both the disruption and the impression management literature, disruptive visions are widespread in business practice. We integrate real options and impression management theories to hypothesize that articulating a disruptive vision increases the likelihood of the venture receiving funding but reduces the amount of funding obtained. A novel dataset of Israeli start&#8208;ups shows that a standard deviation increase in disruptive vision communication increases the odds of receiving a first round of funding by 22 per cent, but reduces amounts of funds received by 24 per cent. A randomized online experiment corroborates these findings and further demonstrates that the expectation of extraordinary returns is the key mechanism driving investors&#8217; sensemaking.


Disruptive Vision
Disruptive innovation theory defines disruptive innovations as innovations with initially inferior performance attributes, with the potential to dethrone incumbent technologies, services and/or business models (Christensen, 1997;Christensen and Raynor, 2003). However, there is a heated ongoing debate of how to define disruptive innovations (e.g., whether they underperform initially, whether they improve over time, whether they are introduced by new entrants, whether they progress toward the mainstream solely through a niche market, etc.) (Christensen et al., 2015;Danneels, 2004;King and Baatartogtokh, 2015;Markides, 2006;Tellis, 2006). The core insight emanating from this debate is that disruptive innovations should be separated from their outcome: disruption (Sood and Tellis, 2011). Understood from a practitioner perspective (The Economist, 2015), old market linkages in a disrupted market or ecosystem become uprooted in favour of new ones. Therefore, a disrupted market or ecosystem hosts new firms, new market leaders, new products, and new ways of doing business. This view also aligns closely with the description of disruption by Christensen et al. (2015, p. 46) as being 'able to successfully challenge established incumbent businesses'. Similarly, Ansari et al. (2016, p. 4) place disruption in ecosystem domains where incumbent business models are disturbed by the adoption of an innovation in that ecosystem. Thus, while the extant research still lacks consensus on the antecedents, drivers, or definition of disruptive innovation, there is more convergence on the generally observed outcomes of disruption. Disruption is contingent upon the persuasion of various stakeholders in the ecosystem, which can be achieved through the entrepreneur's communications (Ansari et al., 2016). Hence, a disruptive vision communicates an image of disruption. A disruptive vision details deficiencies in the current market, and promises a paradigm shift that will mark 'a [considerable] difference or break from the previous business models and products in an industry or market' (Cornelissen, 2013, p. 708). This impending change is framed as an opportunity for improvement and advantage (Mullins and Komisar, 2010). Since fundamental changes tend to arise from innovations (Ireland et al., 2003), disruptive visions cast their images of a disrupted market as completely new approaches to business stemming from innovation. Therefore, a disruptive vision spotlights an innovation that promotes new functionality, formerly unseen in the market, and that purports to achieve conventional market objectives in a very different way. See Appendix for examples within our dataset.

Disruptive Visions and Investment Acquisition
To explain how a disruptive vision affects investor sensemaking, we turn to the literature on impression management and real options theory. Both are often used to explain investment decisions under uncertainty (Huang and Knight, 2017;Trigeorgis and Reuer, 2017). Impression management refers to the entrepreneur's communication of symbolic cues and narratives to investors that, in turn, influence how investors make sense of the venture. Sensemaking is the process by which investors rationalize what the venture is doing and give meaning to its assessment as an investment opportunity (see Navis and Glynn, 2011;Weick et al., 2005). The central premise underlying real options theory is that an investor has the ability or freedom to act (e.g., exercise, defer, expand, or abandon) at any point in time on the options they hold (Klingebiel and Adner, 2015). An early-stage investment can be viewed as a real option since investors have the ability to fund a position later when new details about a venture's prospects arise. The value of a real option is determined by investors' perception of the balance between the venture's potential upside and any associated risks (Hoffmann and Post, 2017). We argue that this perception, and thus real option valuation, can be influenced by an entrepreneur's impression management efforts, on top of traditional data on venture or entrepreneur status, experience, and prior achievements available to investors.
Disruption, if achieved, has the power to create new industry leaders and shift overall market demand from existing products, services, or business models to new ones. A successful disruption may create an industry shake-out, with the candidate venture controlling the dominant design (Argyres et al., 2015), thereby yielding extraordinary returns for the responsible venture and its investors. Thus, ventures can create the expectation of extraordinary returns by communicating a vision of disruption. Such ventures may be alluring options among wider holdings of early-stage investments, since returns in such portfolios tend to follow the power law whereby the best-return investment exceeds the combined returns of all remaining investment options (Maples, 2016). Therefore, a single huge success can ensure the viability of the investor's entire portfolio (Ruhnka and Young, 1991).
Conversely, images of disruption may also be associated with greater potential exposure to uncertainty. Nonetheless, investors are often prepared to accept risk of the unknown if the focal venture has a chance of becoming a great success (Huang and Pearce, 2015). Here, a large gain not only ensures portfolio viability, but also improves public image among fellow investors (Dimov et al., 2007;Gompers, 1996). Moreover, risk tolerance is bolstered when the option permits the exercising or abandoning of an investment at a later stage, when the speculative risks become clearer.
A highly disruptive vision also instills a fear of missing out on the next big change in the market. Investors may act on the anticipated regret of forgone extraordinary returns. This is especially the case when the investors face the prospect of a competitor capitalizing on the ensuing upheaval in the marketplace and the extraordinary returns associated with such a change (Hooshangi and Loewenstein, 2018). Hence, a fear of missing out a potentially significant investment opportunity may drive investors to select the venture as an investment option.
Furthermore, since a venture's vision of disruption implies the potential loss of valuable competencies in current market structures and dynamics (Henderson, 2006), as well as potential obsolescence in an investor's current portfolio, market linkages between ecosystem participants may not persist. This drives investors to select an option that hedges against the potential loss of market access and increases the flexibility to exercise diversified strategic alternatives at a later stage. Consequently, early-stage investors may be prompted by disruptive vision communication to see the venture as an option for future extraordinary returns. Therefore, we argue that: Hypothesis 1: The more disruptive a venture's vision communication, the higher the likelihood of attracting financial investments.

Disruptive Visions and Amount of Investment Acquired
We hypothesize a negative effect of disruptive vision on the amount of funding provided by investors. We return to real options theory and impression management literature to elaborate the negative effect of disruptive vision. Because options (e.g., the right to increase or abandon an investment) can be exercised at later stages of market development when the level of uncertainty regarding the new venture has reduced, there is less incentive for investors to provide large amounts of capital during initial stages (Klingebiel and Adner, 2015). While investments in all young ventures are risky and uncertain, the perception of this risk and uncertainty is largely shaped by how the entrepreneurs communicate their visions and form impressions in the minds of potential investors (Huang and Pearce, 2015;Lounsbury and Glynn, 2001). These perceptions affect the amount of funding acquired from investors. Articulation of a highly disruptive vision increases uncertainty about the outcome. The more disruptive the vision, the more likely is the investors' perception that a venture may need to diverge from specific plans (Garud et al., 2014). Additionally, research has shown that excessive promotions of innovation and novelty force investors to weigh the challenges in commercializing the innovation more carefully (Dimov and Murray, 2008;Parhankangas and Ehrlich, 2014) and may point investors toward the possibility that unknown fatal flaws in the business idea exist (Maxwell et al., 2011).
A disruptive vision thus discourages high-volume stakes in a venture. This is because investors tend to be risk-averse toward low probabilities of success that hinder overall portfolio returns (Tversky and Kahneman, 1992). Instead, investors take smaller positions (i.e., investments) in a venture that communicates a more disruptive vision than in a less disruptive one, and await market news before exercising further options. We argue that the communication of a disruptive vision has a direct negative effect on the amount of financial funding in a first investment round. Therefore, we hypothesize that: Hypothesis 2: Communicating a more disruptive vision lowers the amount of venture funding.

Expectation of Extraordinary Returns and its Mediating Effects
When ventures successfully 'disrupt' the status quo of existing products, firms, or markets, they may create an industry shake-out with the candidate venture becoming the dominant player. Ventures that communicate a disruptive vision often promise huge opportunities for investors. However, disruption is difficult to achieve and the necessary steps and timing are largely unknown. The distant and volatile nature of disruption © 2018 The Authors Journal of Management Studies published by John Wiley & Sons Ltd and Society for the Advancement of Management Studies entails high risks that are unknowable. The tension between the great potential opportunity and the endemic riskiness fosters an investor mindset that a venture's business idea is 'something so ridiculous that it could actually work' (Huang and Pearce, 2015, p. 641), possibly generating returns on investment (ROI) of tenfold or better (Sahlman, 1990) through an Initial Public Offering (IPO) or exit sale to another entity (Prowse, 1998). Overall, this game-changing appeal of a disruptive vision lures investors with the expectation of a significant investment outcome among a portfolio of early-stage investments.
The expectation of extraordinary returns logically increases the likelihood of funding. Investors naturally pursue unconventionally high investment returns (Huang and Pearce, 2015). Yet, early-stage investments are also associated with higher likelihood of subsequent losses. As an offset, early-stage investors expect exceptionally high rates of return (Ruhnka and Young, 1991) that help ensure the viability of their portfolios (Maples, 2016).
Moreover, seizing investment opportunities that yield large ROIs increases the visibility and standing of investors among fellow capitalists (Dimov et al., 2007). For example, early investors in ventures that disrupt markets and ecosystems are often celebrated in entrepreneurial circles (e.g., Peter Thiel for Facebook; Jeremy Liew's Lightspeed Venture Partners for Snapchat; Chris Fralic's first round capital for Uber). Such gains in visibility are important as they may attract larger capital flows to the investor's fund later (Gompers, 1996). In addition, leaving such an opportunity unexploited adds to the anticipated regret of missing out on the potential monetary and social gains.
In contrast, the lack of a disruptive vision may cool expectation of extraordinary returns, hampering the venture's profile as a valuable investment option among others. Thus, the stronger the expectation of extraordinary returns created by a disruptive vision, the more likely it is that investors will take an option in the venture.
Hypothesis 3: The positive relationship between the disruptiveness of a venture's vision communication and the likelihood of attracting financial investments is mediated by the investor's expectation of extraordinary returns.
Arguably, investors who perceive a venture as likely to offer extraordinary returns might also increase their stakes in that venture. For example, if investors believe it to be highly likely that the venture will increase its valuation tenfold within five years, they may be more inclined to capitalize on the opportunity, seeking a higher stake in the venture and thus endowing the venture with more financial capital. In such a case, there should be a positive relationship between the expectation of extraordinary returns and the amount funded. Because highly disruptive visions positively affect the expectation of extraordinary return, we argue that disruptive visions also exert a positive, indirect impact on the amount of funding from investors (i.e., similar to our arguments for Hypothesis 3) through the expectation of extraordinary returns.
Despite this positive, indirect effect of a disruptive vision through the expectation of extraordinary returns, we still expect a negative, direct effect of the disruptive vision on funding amounts (see arguments for Hypothesis 2). This is called inconsistent mediation (for details, see Aguinis et al., 2017;MacKinnon et al., 2007; Gardner et al., 2011;Jayasinghe, 2016). With inconsistent mediation, the direct effect of the independent variable has an opposing sign to the mediated effect. Incurring the opposite mediating effect from the expectation of extraordinary returns helps expose the direct negative effect of the disruptive vision on the amounts of funding acquired.
Hypothesis 4: Expectation of extraordinary returns mediates the relationship between the disruptiveness of a venture's vision communication and the amount of venture funding.

OVERVIEW OF STUDIES
The aim of our paper is to investigate the efficacy of disruptive visions for acquiring a first round of funding. We tested our hypotheses using two complementary studies. Our first study uses an archive of Israeli start-ups. With this study, we empirically tested the main effects of disruptive visions on investment decisions (i.e., Hypotheses 1 and 2). This field study also provided ecological validity for our findings. Study 2 was comprised of a randomized online experiment that both replicated findings from the first study and identified the mechanism underlying the positive effects of disruptive visions on investment decisions (i.e., Hypotheses 3 and 4). This experimental study generalized our findings beyond the Israeli venture context, and the randomized control nature of the experiment pinpointed the causality driving our results.

Sample
We test our hypotheses using a comprehensive database of Israeli start-ups. Israel is often dubbed a 'Start-up Nation' for its strong entrepreneurship scene, having the most hightech start-ups per capita (Senor and Singer, 2009) and a vibrant venture capital scene (Avnimelech and Teubal, 2006). Israeli start-ups are young, internationally oriented, knowledge-intensive organizations that mainly produce innovative, proprietary self-developed technologies (Engel and del-Palacio, 2011). We obtained data from Start-Up Nation Central -a private non-profit organization that has exhaustively collected and accurately stored data on all Israeli start-ups since 2013 (www.startupnationcentral.org). The data featured on Start-Up Nation Finder (Start-Up Nation Central's 'Innovation Discovery Platform', https://finder.startupnationcentral.org) provide detailed information on venture activities, products, locations, founders, management teams, funding, and investors. This dataset is uniquely qualified for testing our hypotheses for two reasons. First, it offers rich and reliable information on venture, entrepreneur, and funding outcomes. Second, the data allow us to correct for selection bias since they include firms that obtained funds and those that did not. Prior research has mainly considered ventures that have already obtained funding (e.g., Gompers, 1995;Kanze and Iyengar, 2017;Ter Wal et al., 2016), creating a methodological sample-selection problem. With our data, we can regress the models on both the likelihood of funding and the amount of funding to properly correct for selection bias.
We sampled ventures founded between 2013 (when Start-Up Nation Central began) and 2016, including only their first round of funding (Seed or A round). Our cross-sectional sample totals 2139 ventures. We randomly chose 1000 start-up firms from this sample. After removing missing values for the variables selected in our models, the final dataset contained 918 start-ups.

Measures
Dependent Variables. We coded ventures that had first-round funding as investment received (1 if yes, 0 otherwise). The amount of funding received was measured as the amount of funding in US dollars that a venture received in its first funding round. Generally, the first funding round referred to a Seed round, but in some cases, ventures skipped the Seed round and went straight to the A series -a recent trend known as bootstrapping (Newlands, 2015). We applied the natural log of this variable because of skewness (Skewness = 4.17, Kurtosis = 21.92, Shapiro-Wilk test W = 0.56, p < 0.001). Independent Variables. We followed the standard practice of coding vision statements (e.g., Baum et al., 1998;Baum and Locke, 2004;Berson et al., 2001) to measure disruptive vision. Vision statements were displayed on the Start-Up Nation Finder for investors. Since the Start-Up Nation Finder platform is used by investors to seek and select promising start-ups, these statements are important in entrepreneurs' communication with investors. Two graduate assistants coded the vision statements. After initial instruction meetings and resolution of disagreements on a trial set of vision statements, the two coders were directed to proceed in isolation and refrain from any further discussion.
A disruptive vision conveys a drastic change in the way organizations or ecosystems operate, showcasing a significant break from existing products, services, and business models (Cornelissen, 2013). Since fundamental changes tend to emerge from innovations (Ireland et al., 2003), disruptive visions evoke images of a disrupted market and a new approach to business stemming from innovation. Therefore, we operationalized disruptive vision using the following four items indicating (1 if yes, 0 otherwise) whether the vision statement (i) 'promotes drastic [or fundamental] change in the future: it makes a claim of pursuing dramatic change at a market or larger level, with implicit consequences for multiple stakeholders' (Kappa = 0.61); (ii) 'features a future that contrasts with the status quo: it delineates deficiencies in the current market situation and promises a substantial improvement' (Kappa = 0.66); (iii) 'includes ideas, plans or other evidence of achieving the conventional market objective in a completely different manner' (Kappa = 0.46); and (iv) 'promotes the venture's innovation or activities as enabling a completely new function' (Kappa = 0.21). Because of the low Kappa value of the last item, we removed it from our Control Variables. We drew from prior literature to identify four sets of control variables in our models related to the characteristics of the venture and its communications, the founders, the product and market, and the funding round.
The first set of controls included traits of a venture's communication style and reach. We controlled for a venture's social media exposure, since this may increase the visibility of the venture and enhance investor awareness (Fischer and Reuber, 2011). Start-Up Nation Finder displays direct links to various social media platforms (i.e., Facebook, LinkedIn, Google+, and Twitter). We operationalized social media exposure by measuring the number of social media platforms for which the venture had a link in the Start-Up Nation Finder database.
We also controlled for the extent to which a venture's vision statement includes the promotion of achievements. Investors may conduct their own due diligence about a venture's and its entrepreneur's achievements, having alternative sources to assess claims. However, prior research on impression management agrees that investors also rely on cues conveyed by entrepreneurs. In particular, the emphasis on achievements may be an important determinant of the credibility and legitimacy of a venture's claims in the eyes of investors. The coders rated each company statement regarding three items indicating (1 if yes, 0 otherwise) whether it (i) 'features evidence of past performance/experience of entrepreneurs and employees' (Kappa = 0.69); (ii) 'presents evidence of past and current successes of the venture in the market, including customers, locations, market leadership, and awards and prizes' (Kappa = 0.63); and (iii) 'features claims of accrued resources, such as the latest/proprietary technology, partnerships/networks/affiliations, and patents/prototypes' (Kappa = 0.61). Both coders had high agreement across items per vision statement (mean Rwg = 0.88). Next, the average for the two coders was computed for each item, and resulting averages were then summed to calculate a score per venture. The coders showed good agreement and reliability in the summed promotion of achievements measure (mean ICC2 = 0.84).
Vision communication is often associated with imagery (Emrich et al., 2001). Messages high in imagery induce more vivid portraits of what is communicated (Carton et al., 2014). We controlled for imagery to isolate the effect of disruptive visions beyond imagery. We used the Toronto Word Pool, which rates words on degrees of imagery using a 1-to-7 scale (Friendly et al., 1982). Imagery scores were then averaged for the words in a venture's vision statement.
The second set of controls pertained to features of the venture itself. Venture capitalists and angel investors who focus on early-stage investments are more likely to favour younger ventures (Huang and Pearce, 2015;Ter Wal et al., 2016). Therefore, we controlled for venture age by subtracting the year of founding from 2016. Furthermore, if start-ups stated in the vision statement that they were a part of another firm, we coded © 2018 The Authors Journal of Management Studies published by John Wiley & Sons Ltd and Society for the Advancement of Management Studies them as Subsidiary ventures. We included this as a control since these ventures may require and receive different levels of external funding due to affiliation with a larger established firm (D'Mello et al., 2008). We also coded whether ventures were members of (corporate) accelerators, co-working environments, or entrepreneurship programs, as these relationships assist ventures in developing their activities, markets, strategy, and resources. These programs may also offer networking, educational, mentorship, and pitch-making opportunities (Cohen, 2013). We mark ventures as a Member of a program using a dummy variable in our models (member = 1, non-member = 0).
Additionally, Start-Up Nation Finder displays categorizing tags on a venture's page. By clicking a tag, ventures with similar characteristics can be found. By including the Number of tags in our models, we controlled the exposure to investors through Start-Up Nation Finder. This skewed variable was log-transformed (Skewness = 1.03, Kurtosis = 3.73, W = 0.95, p < 0.001). Finally, ventures in our dataset were assigned to one sector: software, healthcare, security and safety technologies, or other. We included sector dummies because funding requirements and timing vary across sectors.
The third set of controls pertains to founder, and product and market characteristics. We controlled for serial entrepreneurship. Serial entrepreneurs can call upon amassed experience and networks that enable access to valuable resources (Cassar, 2014). We coded Serial entrepreneur as 1 if a (co-)founder appeared as a (co-)founder of another start-up in our full database (i.e., including all ventures in the Start-Up Nation Finder database that were founded before 2017). We controlled for geographic scope since the number of target markets can affect sales and growth potential as well as capital needs in serving different markets (Gupta and Sapienza, 1992). Start-Up Nation Finder lists each start-up's geographical target markets. Geographic areas included North and South America, Europe, Asia, Africa, the Middle East, and Oceania. Geographic scope was proxied by tallying the regions where a venture was active. Furthermore, products in research and development phases are riskier investments than those already launched (Audretsch et al., 2012). We controlled for the stage of development by including a dummy variable, Released product, marked as '1' when a venture's products were released commercially, or as '0' otherwise.
Finally, we included two control variables for a venture's first-round funding. In our analysis, we included only ventures initiating Series A or Seed funding. Generally, funding levels increase with the funding series, and start-ups can leapfrog through bootstrapping -i.e., building and growing a venture with personal finances or using initial operating cash flow (Newlands, 2015). We included a dummy variable in our models for A-Round funding to indicate ventures that bypassed the Seed round and went straight to A-series in their first round. Lastly, we controlled for investor prior experience as this may influence investment decisions (Huang and Pearce, 2015). We operationalized investor experience by averaging the total number of funding rounds the investors took part in before the focal funding round. We calculated this variable using the full database, including all funding rounds in the Start-Up Nation Finder database that occurred prior to 2017.

Analytical Approach
The fact that funding decisions by investors are not random may introduce bias into our coefficient estimates for the amount of funds acquired. To mitigate sample-selection bias induced by a non-random selection of observations for received funds, we applied the Heckman correction using 'full-information maximum-likelihood' estimation (FIML). The FIML estimator offers more efficiency than the two-step estimator (Greene, 2012) since all parameters of the selection and outcome equations are estimated simultaneously using the likelihood function (Certo et al., 2016). Prior research advises an exclusion restriction such that there is at least one variable with a non-zero coefficient in a selection equation estimating acquired funds that is excluded from the outcome equation estimating funding amount (Certo et al., 2016). We used number of tags and social media exposure as exclusionary variables since they proxy the probability that an investor landed at the venture's page on Start-Up Nation Finder via click-through (internal and external, respectively). Both elements primarily influence the awareness of a venture and, thus, its likelihood of funding, but not the amount of funding. After all, the number of tags or social media links is quite uninformative about venture risk or upside potential. In the results section to follow, we discuss diagnostics regarding our selection correction approach.
We used Probit regression to estimate the selection equation for a venture's propensity to receive a first investment round. To test Hypothesis 1 concerning the likelihood of obtaining a first funding round, we conduct and report on a logistic regression instead of the Probit selection equation. 2 The model specification of our logistic regression was identical to that of the Probit selection equation.

RESULTS
We first report the descriptive statistics and bi-variate correlations as model-free evidence. Tables Ia and Ib present descriptive statistics for all variables in Study 1. We observe that ventures were almost two years old on average, operated mostly in one geographic area, and that 43 per cent of ventures operated in the software sector. Furthermore, we note that 38 per cent of ventures had released products, 47 per cent were founded by at least one serial entrepreneur, nearly 18 per cent had an A-series as first-round funding, and that 7.5 per cent of ventures were subsidiaries. Importantly, only 14.8 per cent of ventures received an investment, and those that did, acquired an average of $905,227 (geometric mean). Table II presents the Pearson correlations. We observe that venture age has a significant positive association with having received an investment, but a negative association with the amount of funding received. Older ventures also are more likely to release products and to be active in social media. Importantly for the exclusion restrictions, ventures with more links to social platforms and more tags on their Start-Up Nation Finder page were positively correlated with receiving an investment, but not with the amount of investment received. We also observe that the promotion of achievements was positively and significantly correlated with both receiving funding and acquiring higher amounts. Regarding our main variable of interest: we observe a positive significant association of a disruptive vision with receiving an investment; and while not significant, but in line with our inconsistent mediation hypothesis, we note a negative association of a disruptive vision with the amount of funding.

Sample-Selection Correction Diagnostics
Sample selection impacted our data since the independent variable predicted significantly in the selection equation, and rho emerged as significant in our full model (rho = −0.81, S.E. = 0.13, p < 0.001, Model 4 in Table III) (Certo et al., 2016). Moreover, our independent variable did not correlate with error terms of the selection equation (r < 0.01, p = 0.94) or the outcome equation (r < 0.01, p = 0.99), and thus proved to be exogenous. Therefore, we deemed the results of our outcome equation to be unbiased (Certo et al., 2016). Also, the correlation between our independent variable and the inverse Mills ratio was lower than 0.30 in absolute terms (r = −0.24, p < 0.001), indicating sufficient strength for our exclusion restrictions (Certo et al., 2016). Last, a likelihood ratio test (χ 2 = 52.52, df. = 2, p < 0.001) between the over-identified model (i.e., using

Robustness Checks
As seen in Table Ib, the distribution of the dependent variable 'investment received' is skewed with only 14.8 per cent of ventures receiving investment. In our logistic regression models, this may have caused separation 4 (Heinze and Schemper, 2002) or inconsistent parameter estimates (Donkers et al., 2003). We saw no trace of separation in our models. To assess the consistency of parameter estimates, we ran additional analyses using randomly drawn, balanced samples (see the Appendix A for details). Consistent with our main analyses, we observed a significant and positive effect of disruptive visions over 10,000 bootstraps (Odds ratio = 1.33, 95% CI = [1.10, 1.69], p = 0.005).
The fourth item of the disruptive vision measurement yielded a low Cohen's Kappa of 0.21. Therefore, we excluded the item from the measure of disruptive visions in our main analysis. Nevertheless, the item is relevant for theoretical reasons: Central to a new venture's disruptive vision is an innovation (i.e., any novel approach, technology, or business model) allowing it to pursue disruption. When including the focal item in our measure for disruptive vision, the results remained qualitatively similar for both the likelihood of receiving first-round funding (Model 2, Table III

DISCUSSION
Study 1 found that a disruptive vision increased the likelihood of first-round funding while decreasing the amount of funding. Study 1 offered these insights from a unique and relevant empirical field setting that advises both business practitioners and researchers to consider disruptive vision communication when making investment decisions. However, the cross-sectional nature of our archival data limits claims of causality. Also, generalizing the findings requires replication in other contexts, and the lack of data on investor sensemaking did not allow us to investigate the mechanisms driving the results. To address these issues, we conducted a randomized online experiment described next.

Participants
Two hundred and fifty-three people were enlisted on the Prolific.ac website, a platform for surveys and experimental projects. The survey took 12 minutes on average, for which we offered compensation in accordance with Prolific.ac rules. To ensure participant quality, we prescreened according to the following specifications: first, participants had investment experience with exchange-traded commodities or funds, government bonds, stocks, unit trusts, angel (syndicate) investing, private equity funds, venture capital funds, options, or crowdfunding. This ensured a representative sample of respondent investors. Second, task acceptance rates had to exceed 90 per cent. Third, the level of education had to be undergraduate or higher. Fourth, participants had to be at least 25 years old (i.e., no students) with residence in the European Union (including the UK), US, or Australia.
In both the introduction page and in the survey, we included attention checks to filter out participants who answered carelessly. Our final sample comprised 203 participants with 50 per cent female, averaging 40.5 years old (S.D. = 11.19), with 27 per cent having invested in entrepreneurial ventures.

Design
We designed a 2 (low and high disruptive vision) × 2 (low and high promotion of achievements) randomized between-subjects experiment. For each condition, we created a vision statement using the same fictitious venture. The vision statement was based on a venture from our Israeli database, adapted, and edited to match our purposes (See Appendix B).
We anonymized the names of the venture and its founders. To improve the overall credibility of the experiment, we added fictitious company information to the vision statements similar to profiles presented on Start-Up Nation Finder. This information, as well as the formatting and layout of the entire vision statement, was identical across all four conditions. Fictitious profiles featured: founding date, funding stage, geographical target markets, product stage, number of employees, business model, customers, and estimated valuation.

Procedure
The participants first read an introduction page explaining the purpose: to investigate early-stage investment decisions. We also informed them that we would ask them to answer a survey about their investment decisions regarding the venture to be presented. Each participant was randomly assigned a condition and read only the venture vision statement central to that condition. After manipulation checks, participants were asked if and how much they would invest and answered questions to inform our mediator and control variables. The survey ended with a page thanking the participants, informing them of the fictitious nature of the information presented about the venture, and referring them to the Prolific.ac website for compensation.

Dependent Variables
Our two main dependent variables were whether a respondent funded the venture (investment received) and the amount of funding they offered. To mirror the Study 1 analysis, we used the log-transformed values of funding amount in our models. For the investment decision questions, we introduced the following vignette: 'Imagine that you are an investor working for an investment company (e.g., a venture capitalist firm). You have to decide how to invest the $500,000 funds you are managing. You are expected to earn a minimum of 15% return per year on the fund over the next 5 years.
ProSearch is one of several investment opportunities. ProSearch is looking for a $100,000 investment, offering 20% equity ownership (valuing the venture at $500,000).' We next posed the following question to log a participant's investment decision: 'Would you… (1) leave the money in the bank, earning a steady 5% yearly interest rate, and wait for the next investment opportunity or (2) Invest (part of) the money in ProSearch?' To measure the investment amount, we asked (on the next page): 'Regardless of your answer on the previous question, if you were to invest in ProSearch, how much would you invest in exchange for 20% equity ownership in ProSearch?' Participants answered this question on a slider ranging from $1 to $100,000.

Independent Variables
Our manipulation of disruptive vision is detailed in the Appendix B. We incorporated it as a dummy variable in our analyses. 5 For this variable, zero (0) meant survey participants were exposed to low disruptive vision conditions, and one (1) indicated participation in high disruptive vision conditions. We measured expectation of extraordinary returns using four items adapted from Huang and Pearce (2015). We asked using a five-point Likert scale (1: Very unlikely, 5: Very likely): "What do you think is the likelihood ProSearch will achieve one of the following successes?" The outcomes included: 'Being acquired by another firm at a high price', 'Having a successful Initial Public Offering (IPO)', 'Yielding tenfold returns to investors', and 'Becoming a market leader' (Cronbach's Alpha = 0.79).

Control Variables
Distinct from Study 1, our data for Study 2 posed no variation in venture characteristics. Control variables in Study 2 thus pertained only to elements of the manipulation and to investors. Via experimental design, we controlled for promotion of achievements. Similar to our disruptive vision variable, we treated the promotion of achievements as a dummy variable in our analyses. Additionally, we controlled for participants having investment experience in nascent ventures. Experience with early-stage ventures may shift a participant's perception of the attractiveness of the investment opportunity. Since risk preference shapes how willing one is to invest in risky efforts, such as young ventures, we included risk preference as a control variable. Following Koudstaal et al. (2015), we asked the participants to rate on a five-point Likert scale 'How much do you describe yourself as willing to take risks?' We also included participant age and gender as controls. Lastly, to avoid sample-selection bias (that was remedied statistically in Study 1), we included investors declining first-round investment into our regressions on amount. This possibility emerged since we asked respondents to select an amount even when refusing to invest at all. To control for potential variance in the amounts chosen among 'yes' and 'no' investors, we included the investment made variable in our regressions on the amount of funding chosen.

Analysis
We applied logistic regression to estimate likelihood of funding. Ordinary least squares (OLS) regression was used to estimate the effect of disruptive vision on the expectation of extraordinary return and on the amount of funding awarded by participants. To assess mediation, we conducted causal mediation analysis using the 'mediation' package in the statistical software R (R Core Team, 2017; Tingley et al., 2014). Our analysis involved inconsistent mediation, expressed when the sign of the independent variable's effect on the dependent variable negates due to opposing underlying effects (MacKinnon et al., 2007). A common example of this model is the relationship between intelligence and production mistakes as mediated by boredom. In McFatter's (1979) hypothetical example of an assembly-line, intelligent workers easily got bored and made more production mistakes even though smart people tend to be better at preventing production mistakes. As a contradiction, the overall relationship between intelligence and production mistakes measured zero. However, adding boredom as a mediator unveiled the otherwise hidden opposing impact of intelligence versus boredom on production mistakes.

Manipulation Checks
To gauge the effectiveness of our manipulation, we queried the sample on several manipulation checks. To assess the disruptive vision manipulation, we asked participants to answer on a five-point Likert scale (strongly disagree / strongly agree) how much they agreed with these statements: 'ProSearch says it aims to disrupt the product search and discovery industry', and 'ProSearch has a vision about the future of product search and discovery'. One-way ANOVA showed large differences between conditions for the 'disrupt' (F(3, 199) = 43.10, p < 0.001) and 'vision' (F(3, 199) = 2.67, p = 0.049) queries. For the 'disrupt' question, post-hoc contrast analysis indicated significant mean differences between all conditions involving 'high disruptive vision' and those invoking 'low disruptive vision' (mean diff. = 3.60, S.E. = 0.32, p < 0.001; Bonferroni adjusted). For the 'vision' question, a post-hoc contrast analysis of the two conditions involving 'high disruptive vision' showed participants viewing the 'high disruptive vision' conditions as more visionary than those of the 'low disruptive vision' (mean diff. = 0.49, S.E. = 0.18, p = 0.01; Bonferroni adjusted).
To assess the effectiveness of our 'promotion of achievements' manipulation, we asked participants to answer on a five-point Likert scale (strongly disagree / strongly agree) how much they agreed with the statement: 'ProSearch and its founders communicate their accomplishments'. One-way ANOVA showed significant differences between conditions on this question (F(3, 199) = 35.31, p < 0.001). Post-hoc contrast analysis indicated significant mean differences between all conditions involving "high promotion of achievements" versus "low promotion of achievements" (mean diff. = 2.51, S.E. = 0.25, p < 0.001; Bonferroni adjusted). Table V provides the results of our analyses. Model 4 replicated our findings from Study 1 and offered evidence favouring Hypothesis 1. Again, we find that ventures conveying a more disruptive vision are more likely to acquire first-round investment (β = 0.74, S.E. = 0.33, p = 0.023). In our experiment, using a highly disruptive vision (vs. no disruptive vision) increased the odds of receiving funds by 110 per cent. Hypothesis 3 posited that an expectation of extraordinary returns mediates the relationship between the venture's use of a disruptive vision and an investor's investment decision. Model 2 indicated that communicating a highly disruptive vision prompted the expectation of extraordinary returns (β = 0.31, S.E. = 0.13, p = 0.02). Model 5 next showed that an expectation of extraordinary returns significantly increased the likelihood of an investor opting to fund the venture (β = 1.48, S.E. = 0.27, p < 0.001). In our experiment, one standard deviation increase in the expectation of extraordinary returns boosted odds of acquiring an investment 4.41 times. Subsequently, we conducted mediation analysis and detected evidence for the mediating effect of expectation of extraordinary returns (β = 0.08, 95% CI = [0.02, 0.15], p = 0.016, 10 000 bootstraps 6 ), thus supporting Hypothesis 3.

Testing
Models 6 to 8 in Table V show our test of Hypothesis 2 (i.e., communication of a more disruptive vision negatively affects the amount of funding). Model 7 offered initial support for Hypothesis 2, showing significant negative effect of a disruptive vision on the amount of funding (β = −0.25, S.E. = 0.11, p = 0.021). Model 8 clearly showed that the effect of a disruptive vision sharpens when controlling for expectation of extraordinary returns, implying that inconsistent mediation is present. We tested the inconsistent mediating effect of extraordinary returns and found evidence for both Hypothesis 2 and Hypothesis 4 in Model 8. Specifically, expectation of extraordinary returns positively mediated the relationship between the disruptive vision and the amount of funding (β = 0.07, CI = [0.2, 0.17], p = 0.015, 10,000 bootstraps), while a disruptive vision had a significant negative direct effect on the amount of funding (β = −0.29, 95% CI = [−0.49, −0.07], p = 0.006, 10,000 bootstraps). Specifically, touting a highly disruptive vision lowered the amount of funding by 25 per cent when controlling for its positive indirect effect on the amount of funding through an expectation of extraordinary returns.

Robustness Analysis
In Study 2, we asked participants to state the amount of money they would invest in the venture, regardless of whether they decided not to invest initially. Thereby, our analysis of the amount of funding in Study 2 includes potential investors that decided not to invest initially. By doing so, our experiment avoids sample-selection bias by design, rather than statistically correcting for it afterwards. We conducted a subset-analysis for our regression models on amount, excluding people who initially decided not to invest in the venture. Results remained consistent with our main analysis: In comparison to Model 8 in Table  V, we again observed a significant effect of disruptive vision on the amount of funds allocated by investors (β disruptive vision = −0.24, S.E. = 0.10, p = 0.022; β extraordinary return = 0.24, S.E. = 0.06, p < 0.001).

DISCUSSION
Results of Study 2 indicated that a more disruptive vision boosted the likelihood of receiving investment since it creates an expectation of extraordinary return in the investors. Yet, when controlling for this effect on the amounts of funding awarded, we observed that communicating a disruptive vision negatively impacted the amount of funding. Study 2 complemented Study 1 in two very important ways. First, Study 2 replicated findings for Hypotheses 1 and 2 in a randomized controlled setting to uphold the generalizability and the causality of results beyond the cross-sectional nature of an Israeli context. Second, it allowed the discovery of expectation of extraordinary returns as a key mechanism shaping our results from testing Hypotheses 3 and 4.

GENERAL DISCUSSION
Entrepreneurs increasingly talk about 'disruption,' framing their products, technologies and ventures as 'disruptive' to secure financial capital from investors. We set out to investigate what a disruptive vision entails and how it helps or hampers entrepreneurs' efforts to obtain financial investments. Through two complementary studies, we consistently found that highly disruptive visions increased the likelihood of first-round venture funding while also limiting the amount of funding obtained. This finding has important theoretical implications for research on disruptive innovations, real options theory, impression management, and vision communication.

Theoretical Implications
First, our findings demonstrate the importance of disruptive vision as a new form of thematic vision content used by entrepreneurs to promote their innovations. This new form of vision content enriches research on disruptive innovation and the disruption process, which has thus far focused on the process of disruption where underperforming performance attributes gradually satisfy customer needs (Christensen, 1997;Christensen and Raynor, 2003). However, prior research has also documented that not all potentially disruptive innovations 'disrupt' (Sood and Tellis, 2011). This variation signals the existence of factors that have been overlooked in examining what drives the disruption process (King and Baatartogtokh, 2015;Tellis, 2006). We argue that one of these omitted factors is an entrepreneur's vision communication. Recent research has suggested that the process of disruption is best understood from the viewpoint of 'disruptors' and how they frame their ventures (Ansari et al., 2016). Our results show that disruptive visions are more likely to convince investors to get on board -albeit with a smaller amount than for less disruptive narratives. We thereby contribute to recent research on disruptive innovations regarding the ways in which firms can manage their ecosystems through communication and framing (e.g., Ansari et al., 2016;Gurses and Ozcan, 2015).
Second, we explore how a particular way of framing an entrepreneurial venture can affect investors' sensemaking of the venture as an investment opportunity. In that light, we contribute to burgeoning research on entrepreneurs' efforts in managing the impressions of stakeholders (e.g., Fisher et al., 2017;Martens et al., 2007;Navis and Glynn, 2011;van Werven et al., 2015;Zott and Huy, 2007). Impression management research has recognized the importance of future-oriented communications (Garud et al., 2014), but has not yet studied the impact of such communications on venture-level outcomes. To address this oversight, we highlight the importance of investigating vision communication as part of impression management efforts.
Vision communication is a category of impression management (see Zott and Huy, 2007, p. 72). In contrast to other forms of impression management, visions showcase the future -that is, what the venture/entrepreneur will do and become. As such, the aim of entrepreneurial vision communication is to affect audiences' perceptions of the intrinsic or substantive value of what the venture aims to achieve. While prior research has empirically investigated how ventures legitimize identity claims, it has focused predominantly on backward-looking communication. However, 'track record touting' alone does not explain how entrepreneurs build trust toward their ventures' proposed activities. With our investigation of entrepreneurial visions, we address this caveat and help scholars to understand how future-oriented communications and their contents shape investor sensemaking. In particular, we not only debut and affirm the gravity of disruptive visions (i.e., increasing the probability of acquiring funding from investors), but also uncover a potential downside to such communications (i.e., attracting lower amounts of funding from investors).
In addition, our findings enhance the understanding of entrepreneurs' impression management approaches in their quest for acquiring investments. Past research has elaborated on various aspects of entrepreneurial communications that affect investment acquisition through the establishment of optimal distinctiveness. For example, researchers have noted that elements such as aggressiveness, assertiveness, competitiveness, and blasting are powerful tools entrepreneurs may use to distinguish themselves in the eyes of investors (Parhankangas and Ehrlich, 2014). Similarly entrepreneurs may use the communication of accomplishments and accrued resources to legitimize their identity claims (e.g., Bernstein et al., 2017;Burton et al., 2002;Hallen, 2008;Martens et al., 2007;Navis and Glynn, 2011;Zott and Huy, 2007). Many of these impression management tactics and symbolic aspects yield synergistic effects that strengthen a distinct, collective perception about ventures and entrepreneurs (e.g., a high growth venture, Baum et al., 1998; an aspiring market leader, Martens et al., 2007;a collaborator or competitor, Ansari et al., 2016). Articulation of integrative themes may serve special purposes for entrepreneurs. For example, research by Hallen (2008) has suggested that communication of prior accomplishments helps young ventures to form notable ties with key ecosystem members. By conceptualizing disruptive visions, we promote valuable understanding of integrative themes in entrepreneurial communications; this opens the door for future research to further investigate how particular content in an entrepreneurial communication may influence audience sensemaking and venture outcomes. Third, we provide new insights regarding both real options and impression management theories to explain investment decisions under uncertainty. On the one hand, the main underlying rationale in impression management theory is that the ambiguity and uncertainty surrounding entrepreneurial activities make it hard for investors to assess the quality of a venture's value proposition. This is why investors rely on cues communicated through entrepreneur's impression management efforts. Still, it is important to note that impression management research alone does not fully explain our findings. Indeed, this stream of research would predict a positive effect of disruptive vision on amounts of funding, since the use of disruptive visions has become increasingly popular (Christensen et al., 2015), and entrepreneurs derive legitimacy for their touted identities from innovation, novelty, and publicized change (Navis and Glynn, 2011). On the other hand, real options theory builds its arguments on the intricate balance between upside potential and risk that motivates real options logic in investors. However, this stream of research cannot explain how communicating a disruptive vision would affect investor real option decision making when it has thus far overlooked the role of impression management (see Trigeorgis and Reuer, 2017). We combine both streams of research to explain how disruptive visions affect investment decisions. Here, our work shows that impression management efforts, such as disruptive visions, may have variant effects on how investors select ventures as real options and how they allocate funds to them. In so doing, we not only draw from these research streams, but also significantly advance them and motivate researchers in both fields to integrate the two in understanding investor decision making.
Fourth, our results challenge prior research that has highlighted unilateral positive returns from strong vision communication (Baum and Locke, 2004;Baum et al., 1998;Van Knippenberg and Stam, 2014). We engage the calls for research into vision content (for a recent review, see Van Knippenberg and Stam, 2014) , contrasts, lists, puzzles, alliteration, imagery, analogies and metaphors, classification, generalization, and authority (Carton et al., 2014;Conger, 1991;Den Hartog and Verburg, 1998;Hill and Levenhagen, 1995;van Werven et al., 2015). Yet, these investigations omit the influence of vision content. Our emphasis on vision content allows a more in-depth understanding of the vision content-vision pursuit relationship , reminding scholars that the framing of visions is an essential part of an entrepreneur's communications, but may have downsides that should be duly investigated.

Managerial Implications
Our findings have strong implications for entrepreneurs. Entrepreneurs must be made aware that the content of their vision communication affects investors' perceptions of their venture. The vision statement plays a critical role in communicating the goal and purpose of the organization and must be crafted with care. In particular, despite the popularity of disruptive visions in practice, our study suggests that entrepreneurs should use them judiciously. While communicating a highly disruptive vision increases the likelihood of receiving an investment, it subsequently reduces the amount of funds endowed in that investment. Furthermore, our operationalization of disruptive vision provides entrepreneurs with a template for the key characteristics of a specific form of vision content, allowing them to craft vision statements in ways that exploit or avoid communicating a disruptive vision. Expanding beyond prior vision communication research, our study specifically enables entrepreneurs to purposefully evaluate the content of their vision statements based on a pre-defined set of items, granting them greater control over their impression management efforts.

Limitations and Directions for Future Research
Investors' funding decisions are more complex than we explain in our study, since the investment process is inherently multistage and involves communications at each stage (Eckhardt et al., 2006;Gompers et al., 2016). While we show that entrepreneurial visions matter at the first stage in the investor selection process, future research should assess the consequences of entrepreneurial vision communication at later stages in the funding process, such as when moving toward an IPO.
Since visions of entrepreneurs regarding their ventures are not static, neither are disruptive visions. Entrepreneurs may revise their visions over time as they acquire new information or experience. Rapid achievements may trigger the creation of grander visions, and failures could serve as reality checks that, instead, moderate visions. Recent research has advised that ventures presenting disruptive frames may go on to alter their communications to be more respectful of competitive pressures (Ansari et al., 2016;Gurses and Ozcan, 2015). Since refinements are rare in the early stages of venture funding (e.g., the first funding rounds), we do not expect this factor to affect our results. Future research can examine changes in vision content over time.
Finally, we acknowledge that firms can also promote themselves as disruptors or as having achieved disruption. Thus, disruption can also form an integral part of how ventures craft their identities in their communications about the past. However, our current operationalization of promotion of achievements did not consider this, since it is unlikely that younger ventures can legitimately claim much history of successful disruption. Future research can investigate whether older, more established ventures may also frame their identities around disruption and if this helps them in acquiring funds during later rounds.

ACKNOWLEDGMENT
We thank Dries Faems, Shaz Ansari, Raghu Garud and Arun Kumaraswamy as well as three anonymous reviewers for their detailed and highly constructive feedback. Earlier versions of this manuscript benefited from the feedback provided by Lindy Greer, Pavel Zhelyazkov, and seminar participants at Israel Strategy Conference, Academy of Management Conference, DRUID, and University of Lugano. We would also like to thank Startup Nation Central Ltd. and Lior Karol for granting access to the proprietary data and for the invaluable collaboration.

NOTES
[1] We tested and ascertained the robustness of our findings when this item is included. See the Robustness Checks section of Study 1 in the Results section.
[2] Although logistic regression and Probit regressions provide similar results and conclusions, the interpretation of their coefficients is not identical because of the difference in link functions (Greene, 2012). In a Probit model, the value of a coefficient is understood as the increase in z-value on a cumulative distribution function. This can be used to determine marginal probabilities, which are contingent on the chosen values of other variables entered into the Probit model. [3] We used our model estimations to predict values for typical ventures. We only varied disruptive vision and round type, taking the average value for all other continuous variables and the most frequent value for dummy variables. We let the disruptive vision variable vary from its lowest to its highest possible value, in one standard deviation increments. To calculate the average dollar impact, we took the average of the differences between subsequent predicted values. Since our dependent variable was log-transformed, we corrected the predicted values for the logarithmic scale in accordance with Duan (1983) [4] In the fitting process of a logistic model, separation (or monotone likelihood) can occur if the likelihood converges while at least one parameter estimate diverges to infinity. We applied the 'detect separation' function from the 'brglm2' package in the statistical software R (Kosmidis et al., in press; R Core Team, 2017).
[5] Since our experimental manipulations used a 2×2 disruptive vision-promotion of achievements design, we included the interaction term in our models as a robustness check. By doing so, we aimed to rule out the 'high disruptive vision-high promotion of achievements' condition as driving the observed effects. In none of our models did we find strong evidence of an interaction effect (lowest p-value = 0.09; interaction added to Model 8, Table A1).

APPENDIX A. B A L A NCE D S A M P L E BOOTST R A P A NA LYSIS
To assess the robustness of our parameter estimates in Study 1, where data showed imbalance in the dependent variable of our logistic regression, we ran a balanced sample non-parametric bootstrap analysis (as suggested by Donkers et al., 2003). For each bootstrap iteration, we randomly drew (with replacement) a subsample of ventures in which no investment was made, equal in size to the subsample of ventures that did obtain investment. Since we observed 116 ventures receiving first-round investment, we randomly drew 116 ventures that did not. Each bootstrap thus features a sample size of N = 232. Since each logistic regression was estimated on its own log scale, we may compare only standardized effects, even when model specifications remain identical for subsequent analyses. Therefore, we used the odds ratio for each bootstrap coefficient to compute mean bootstrapped effects for each variable. Additionally, we report bias-corrected and accelerated confidence intervals below (DiCiccio and Efron, 1996), as well as p-values that correspond to the proportion of coefficients opposing the reported effects (Tingley et al., 2014). To clarify, a p-value of 0.01 signifies a 1 per cent chance that (given our bootstrapped analysis) the odds ratio is actually 1. We used 10,000 bootstraps with bias-corrected and accelerated (BCa) confidence intervals.

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Our vision
ProSearch will change the way in which people search and discover new products. We have envisioned a disruptive product search technology tailored to the highly competitive e-commerce industry. With our new cutting-edge approach to cleaning and structuring data, we enable a search experience that revolutionizes the capabilities of major search and e-commerce companies in understanding their users' needs and providing qualified, relevant results. This inevitably boosts relevancy and conversion rates, leading to greater profitability. We will disrupt the world of e-commerce and become the global leader in product search and discovery! providing a robust data infrastructure for matching products to user intent with an unprecedented degree of accuracy, nuance, and coverage. We understood that, with our approach to cleaning and structuring data, we could enable a search experience that would revolutionize the capabilities of major search and e-commerce companies to understand their users' needs. Today, our unique approach to classifying, recommending, and displaying products significantly outperforms existing search technologies, including those of major retailers and search engines.
Using our expertise in search and data science, we make products and product-related information easily accessible and incredibly useful to users, which ultimately removes barriers in dealing with product selection and purchase decisions. Resources JAGO: "JAGO's PlayDate platform empowers parents to schedule, manage, and track their kids' social lives in a single intuitive app. Our vision is to transform the way parents manage their kids' social lives, changing the paradigm from on-the-fly arrangements to seamless scheduling and management. We anticipate a time where family event planning will be as organized and controlled as a workplace calendar. The system's ability to manage the digital relationships between children, their friends, parents, additional caregivers, and parents of friends is the essence of the application. Our technological vision is to capture, manage, and enhance those relationships." Yes Yes Yes

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Fone.do: "Fone.do is developing a revolutionary simple to use, low cost intelligent business phone system you can set up yourself in a few minutes. We do things differently. Fone.do is a complete phone system for small business. You can use it from your web browser, mobile, and desk phones. One number that receives calls everywhere, anytime. We are proud that the Fone.do team is made up of creative people from diverse backgrounds. Together we are changing the way small businesses communicate." AppInsight: "AppInsight generates security vulnerability reports for any app, along with detailed remediation guidelines, automatically and without any integration to the development process.

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We continuously scan your apps for the latest known vulnerabilities and proactively alert you when new threats to your app are detected, prioritized according to severity level. AppInsight ranks the security posture of your apps, compared to millions of apps that have already been analyzed. By doing so, we have built a huge database of mobile apps. Our group of top notch security experts from the IDF elite intelligence corps leverages their extensive experience and best practices to help you rectify every detected vulnerability and threat. Our vision is to create the industry standard for mobile application security." Resources BigaBid: "BigaBid is a data driven, data science focused solution designed for performance marketing in the mobile advertising industry. Through real time optimization and user profiling, BigaBid leverages unrefined user data and aggregates it into actionable audience profiles based on key behavior, location, and content trends. The data above is obtained by us through both our ongoing media acquisition activities, as well as through studying the advertiser's historical data. We zoom in on the most relevant, highly actionable users and build profiles based on them. These profiles, when coupled with multiple layers of mobile-specific data, and with the scale and reach of programmatic media buying, allow us to hit your KPIs on every single ad campaign, eliminate wastage and improve our predictive abilities toward your next campaign with us." CoolaData: "CoolaData is a cloud-based solution which covers all big data infrastructure components for data tracking, warehousing, ETL, data enrichment -all the way to the visualization layer. It's an open data service that is not limited to proprietary tools. Connect to your data, to all your data points -It unifies data from a multitude of external and internal sources, inspects it as one unit, and visualizes the most comprehensive business insights from a richer analytical framework. CoolaData helps to analyze, visualize, predict, and act on big data, without dedicated resources. With CoolaData you boost your BI agility and time-to-insight becomes immediate." The disruptive vision items are (left to right): "The vision statement…" "promotes drastic change in the future: it makes a claim of pursuing dramatic change at a market or larger level, with implicit consequences for multiple stakeholders," "features a future that contrasts with the status quo: it delineates deficiencies in the current market situation and promises a substantial improvement," "includes ideas, plans or other evidence of achieving the conventional market objective in a completely different manner" and "promotes the venture's innovation or activities as enabling a completely new function." b People includes the item "features evidence of past performance/experience of entrepreneurs and employees." Venture comprises the item "presents evidence of past and current successes of venture in the market, including customers, locations, market leadership, and awards and prizes" Resources matches the item "features claims of accrued resources, such as latest/proprietary technology, partnerships/networks/affiliations, and patents/prototypes." c The 'new function' item was not included in the empirical measure of disruptive vision due to low Cohen's Kappa reliability.