2.4 Business Failure
On the contrary, why firms fail has been the subject of debate among scholars and researchers. However, before reviewing the contributing factors regarding the failure of startups, it’s important to point out the definition of business failure.
The most two frequently used definitions of failure have been the discontinuance of ownership of the business (Churchill, 1952; Ganguly, 1985; Williams, 1993) and the discontinuance of the business itself (Bates and Nucci, 1989; Dekimpe and Morrison, 1991). More, Fredland and Morris (1976, p. 7) contended that business discontinuance could be a proxy for failure, as discontinuance proposes that resources have been changed to more profitable opportunities. This appears to be a very large definition of failure and would include as failed: businesses that are sold because the owner needs to retire for age or health reasons; businesses that are sold for a profit; and businesses that are sold because the owner simply is willing to shift to another venture.
However, as mentioned by Churchill (1952, p. 13) the sale or liquidation of a business does not essentially mean failure because lot of businesses are given up due to retirement or illness or because of other substitutable opportunities. A third definition of failure that is typically observed in the literature is bankruptcy (Massel, 1978; Hall and Young, 1991). Accordingly, Ulmer and Nielsen (1947, p. 11) defined as failures, those companies that were disposed of (sold or liquidated) with losses to prevent additional losses. Finally, Cochran (1981, p. 52) recommended that “failure should mean inability to ‘make a go of it’, whether losses entail one’s own capital or someone else’s, or indeed, any capital”.
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Despite the believe that the success rate of startups has the ability to dramatically extend economic growth on global scale, very few and controversial findings about their failures have been established by the researchers in the most recent years. A well-known contributor as researcher and practitioner of the startup community is Steve Blank. In his research he found how very few startups fail for lack of technology, relatively they almost always fail for lack of customers. Then, for a corporation attempting to enter a very innovative market without proof of functionality in the real world there are more possibilities of failure.
Another prominent entrepreneur is Eric Ries who observed that startups are failing so badly everywhere because of two main issues. He considered that the traditional management techniques as the allure of a proper plan, a strong strategy, and thorough market research can’t be applied to startups, because startups operate with too much uncertainty where it is difficult to predict the future. Consequently, he stated that some entrepreneurs have thrown up their hands and adopted the “Just Do It” school of startups, after seeing that old management fail to fix the problem, so they think that chaos is the answer.
According to empirical studies of the failure rates of companies among different industries have also persistently proven that new companies have a much higher probability of failure than established companies (Fichman & Levinthal, 1991; Utterback & Suárez, 1993). Between 20% and 30% of new start-ups closes throughout their first year of existence, and the failure rate is 80% within six years of commencement (Dollinger, 2003). Likewise, Audretsch and Mahmood (Audretsch, 1991, 1995; Audretsch and Mahmood, 1991, 1995) also found that new independent companies tend to have a greater hazard rates (i.e., higher possibilities of failing over time, Kalbfleisch and Prentice, 1980) than the new branches of existing companies. Similarly, Agarwal and Gort (1996) realize that survival rates for new startups are industry specific, but in terms of the growth stage of the market. So, their results propose that new companies endure higher hazard rates once the market is in its early stages.
Moreover, Audretsch and Mahmood (Audretsch, 1991, 1995; Audretsch and Mahmood, 1991, 1995) suggest that in an industry that has a routinized technological regime, where incumbents own advantages as innovators, it is harder for new startups to survive. Other studies revealed that one reason behind high failure rates among young firms is a lack of experience and management skills (Humphreys & McClung, 1981; Schwartz, 1976).
More than that, Honjo (2000) conducted two studies regarding the failure of new companies in Japan from 1986 to 1994 — one is based on the age of the company and the other one on the calendar time. He inferred, while financial capital and company size are both substantial predictors of business failure when they are integrated into the model independently, just the financial capital is said to be significant once they are added into the model simultaneously. Honjo concludes that previous research that finds considerable outcomes related to company size, in effect, may also seize the influence of the financial capital. His results also designate that company’s initiated just before a crash or market bubble or just after a market crash are more probable to fail. Else, in the analysis based on calendar time, Honjo found a positive relationship between age and failure, and a negative relationship between age-squared and business failure. Additionally, his results show that a higher entry rate and higher geographical concentration in an industry induce a higher hazard rate for companies.
Even though the mentioned above shows that companies with large startup size and large post entry size are more likely to survive, there are also contradictory findings. For example, Ghemawat and Nalebuff (1985) analyzes an oligopoly situation in a declining industry and indicates that large companies are more expected to exit. Likewise, Lieberman (1990) empirically tests this relationship. Yet, he does not find any evidence of a higher exit rate for large companies. Indeed, Lieberman’s results claimed that in declining industries, large firms are more likely to close individual companies. Else, Das and Srinivasan (1997) find that firms with a larger startup size are more probable to exit.
Furthermore, the company’s brand equity or reputation is often nearly absent (Lohrke et al., 2010; Korunka et al., 2010) and they are often accompanied with a negative image due to their novelty or because they have new products and/or services. These factors create hindrances to the development of social and business relationships primarily based on external interaction and exchange processes, such as establishing stabilized relationships with customers, creditors, suppliers and other organizations.
Above all, accounting and finance researchers applied different accounting ratios and cash flow components in order to predict business bankruptcy. In the middle of these, few models are obtained based on the discounted cash flow analysis in finance. This study contends that a company’s value equals the sum of its future net cash flows discounted at its cost of capital. Gentry, Newbold and Whitford (1985a, 1985b, 1987) determined twelve cash flow components using methods such as multiple discriminant analyses, and probit and logit models, and recognized three variables—dividends, investment and receivables—relate significantly to corporate bankruptcy. On the contrary, there are also contradictory findings in which Casey, Bartczak (1985) and Gombola et al. (1987) state that cash flow from processes is not significantly connected to business bankruptcy. In a recent study, Mossman et al. (1998) found that the predictive accuracy of the cash flow model is the best throughout the last two to three years before bankruptcy. Thus, individuals who decide to become entrepreneurs confront long odds for each survival and success.
