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Introduction

To face the COVID-19 crisis, government institutions, business chambers, and academic centers have called for innovation initiatives, such as the launching of startups (CEPAL, 2020). However, in Mexico, 75 percent of startups closed their business after their second year of existence, which means that only 25 percent of them remain up-to-date (El Financiero, 2016). However, it is not the same for the U.S., considered the leading country in the number of startups created and how they have handled the worst conditions during the COVID-19 pandemic (Minaev, 2021; Djankov & Zhang, 2021). The next normal has triggered and accelerated the shift to the automation and digitization revolution; approximately 39 percent to 58 percent of work worldwide in operationally demanding sectors can be automated using currently demonstrated technologies (McKinsey, 2020a). Surely it is going based on startups (Haltiwanger et al., 2013). Therefore, this research's challenge, usefulness, and originality lie in the proposal of a framework confirmation and the comparison between how startups among the Mexican/American SIS are handling the innovation strategies analyzed through KSF and KFF.

The Oslo Manual and the Business Model Innovation

The last edition Oslo Manual defines innovation (OECD, 2018: 20): "An innovation is a new or improved product or process (or a combination thereof) that differs significantly from the unit's previous products or processes and that has been made available to potential users (product) or brought into use by the unit (process)." Frequently, economic crises and ravages are periods of creative destruction, source of innovation strategies. The broad concept of innovation embraced by the OECD Innovation Strategy emphasizes the need for a better match between supply-side inputs and the demand side, including the role of markets (OECD, 2010). In this regard, the information on the market impacts of a firm's innovation strategies is highly relevant to policy (i.e., the organization of innovation activities within the firm including: the development or modification of an innovation strategy; the establishment or reorganization of units within a firm responsible for innovation; and human resource practices to encourage innovation throughout the firm) (OECD, 2018: par. 5.44 and 8.21).

Hence, we adopted the concept of a SIS as a business model innovation that (OECD, 2018: 242) "… relates to changes in a firm's core business processes as well as in the main products that it sells, currently or in the future" based on one or several sustainable development goals published by United Nations (UN, 2015). Indeed, businesses disturbed by the COVID-19 pandemic were more able to innovate in terms of products and management than those that remained unaffected (Gorzelany-Dziadkowiec, 2021). CEOS agree that innovating the business will be critical because the COVID-19 crisis presents an opportunity that needs to be pursued (McKinsey, 2021).

The Importance of the Startup in Mexico and the U.S.

ASPEN'S report (2017) states that in Mexico, 416 startups were registered, more than half of them aimed to work with social impact interest. Mexico is the country where startup ecosystems are more distributed across its territory, with 32 percent of startups in Mexico City, 10 percent in Guadalajara, and 8 percent in Monterrey (OECD, 2016). According to Statista (2021), in May 2021, there were still 352 working startups, which were aimed at: software data (31 percent ), fintech (23 percent); e-commerce (13 percent ), leisure (9 percent ), health (7 percent), education (4 percent), transport (4 percent), marketing and sales (4 percent), food technology(3 percent), IoT (2 percent), and energy and environment (1 percent). As Minaev (2021) claimed, the U.S. is the leading country in number of startups (around 63,703); over 69 percent of them can become profitable. Minaey also states that the competition (19 percent) is the greatest challenge when starting a business, among other data. In numbers of startups, the US is followed by India with 8,301 startups, and the UK, with 5,377 startups. The U.S. alone has almost three times the amount of startups than the following 9 countries in the world combined. Unfortunately for Mexico, the COVID-19 pandemic and the next normal ravaged that economic backbone by failing to impede the loss of 12.5 million jobs in Mexico. The country's employed population fell from 55.7 million in March to 45.4 million in Apr 2020; this meant the loss of 2.1 million formal jobs versus 10.4 million informal jobs (El Financiero, 2020). For the U.S., the CRS report (2021) informed that, in Apr 2020, the unemployment rates had reached 14.8 percent, while the labor force participation rate declined to 60.2 percent (a level not seen since the early 1970s). This rise in unemployment was caused by an unprecedented loss of 22.1 million jobs between Jan 2020 and Apr 2020. This deterioration in the U.S. labor market corresponded with various advisory or mandated stay-at-home orders implemented in response to the COVID-19 pandemic as well as other pandemic-related factors affecting U.S. demand (CRS, 2021). However, as stated by Djankov and Zhang (2021), contrary to all thought, only in the U.S. did startups grow from 3.5 million in 2019 to 4.4 million in 2020; a 24 percent increase. The number of startups also increased in United Kingdom, Turkey, and Chile. In the U.S., an estimated 9.1. million small businesses were temporarily or permanently closed, even though it is perceived that small businesses create the majority of jobs in the U.S. and other advanced economies. However, research suggests that the new businesses, startups, not small businesses, are the genesis that creates those jobs (Haltiwanger et al., 2013). Some innovative new SIS have responded quickly and flexibly to the pandemic, which is essential to help many countries in this time. The switch to digital education, work, and health services provided innovations in medical goods and services (OECD, 2020). Additionally, the SIS concept is defined here as a startup that is aimed to solve one or several of the 17 sustainable development goals determined by the United Nations (UN, 2015). Despite all the above, most startups have a common denominator: they usually fail. Hence, this study aims to determine the factors and indicators involved that will create a reliable business model innovation scale, capable of maintaining the successful momentum of the startups that respond quickly to market changes, focus on results, and deliver value to customers (McKinsey, 2020b).

Why Does SIS Fail?

More than two-thirds of SIS never deliver a positive return to investors. Why do so many end disappointingly? Many people are inclined to attribute the failure to the inadequacies of its founders, in particular, their lack of grit, industry acumen, or leadership ability. However, blaming the founders oversimplifies a complex situation (Eisenmann, 2021). Hence, it is necessary to identify the main reasons for such a problem and propose a conceptual model to solve it. See Table 1.

The Key Success Factors (KSF) for Social Impact Startup (SIS) Framework in COVID-19 Times

The lockdown measures as a response to the spread of the new coronavirus threaten the existence of many innovative startups. While several of them are successfully leveraging their available resources as a first response to the crisis, their growth and innovation potential are at risk (Kuckertz et al., 2020). Hence, in this analysis we propose the scale based on Mejía-Trejo's framework (for more details, see 2021) to measure the resources as KSF-SIS involving 6 underlying factors: Entrepreneur Profile (EPR); Market Knowledge (MKK); Strategic Analysis (STA); Key Performance Indicators (KPI); Business Plan (BPL); and Value Proposition (VPN). This is a reflective framework designed with 30 independent variables, and 30 items displayed in Figure 1.

Finally, the KSF in SIS scale design is based on the definition of constructs and sources used in the literature (Mejía-Trejo, 2019c). The framework is shown in Appendix. The concept of KSF in SIS here is about their survival based on Aspen (2017) report from Jan-Jun 2021. In the concept of Key Fail Factors (KFF), all the factors involved as KSF are just the opposite of such a framework.

Describing the Final Conceptual Model Proposal and Research Hypotheses

The six constructs' set produces the main reason for our interest, the key success factors for social impact startups (KSF). The six constructs are the causal conditions (independent factors) aligned to predict the outcome. These six sets of causal conditions factors are entrepreneur profile (EPR), market knowledge (MKK), strategic analysis (STA), business performance indicators (BPI), business plan (BPL), and Value Proposition (VPN). Hence, we propose the following hypotheses to highlight the differences between Mexican and American SIS. See Table 2.

Research Method

We summarized the process in the Table 3.

Results

The results are based on CB-SEM and fsQCA techniques as follows:

The CB-SEM Analysis Technique

The measurement framework's validity used the CB-SEM with EQS 6.2 software and applied the maximum likelihood method (Byrne, 2006; Mejía-Trejo, 2020) for the 100/300 Mexican/American SIS in this research. To prove the measurement scale's reliability, for each factor, we computed the Cronbach's Alpha and Composite Reliability Index (CRI) (Bagozzi and Yi, 1988) with results that exceeded the recommended value of 0.7 for both. This means evidence to prove the scale's internal reliability (Nunnally and Bernstein, 1994; Hair et al., 2010). Average Variance Extracted (AVE) is represented from the fundamental construct and the observed variables (Fornell and Larcker, 1981).

According to the Mexican/American SIS, our arbitrary values to accept/reject our hypotheses are stated in a standardized path coefficient (ẞ) >= 0.7. CB-SEM results are shown in Table 4 for the Mexican case and Table 5 for the American case.

However, traditional statistical methods (such as CB-SEM and Multiple Regression Analysis) are intrinsically limited in explaining the effects of complex interaction (of three or more contributing factors) (Ragin, 2008). The fsQCA provides suitable methods to adapt to the complex complementary and nonlinear relationships between structures (Ganter and Hecker, 2014; Woodside, 2013). Hence, we have:

H7: "There is no single best combination, considered as fail success factors, that inhibit strategies business improvement for the next normal."

The fsQCA Findings

The necessary and sufficiency conditions analyses based on fsQCA3.0 software show findings according to the CEOS' configurations for negated KSF (key success factors) for SIS. See Table 6.

For Mexican SIS, we obtained 5 useful patterns with the same outcome, the key fail factors (KFF) or negation of key success factors (~KSF) given the high values of raw coverage, unique coverage, and consistency, shown as follows:

These equations are strongly recommended to avoid, because these combinations are key fail factors (KFF) in social impact startup (SIS). For American SIS, we obtained 2 useful patterns with the same outcome, the key fail factors (KFF) or negation of key success factors (~KSF). Because of the low values of raw coverage, unique coverage, and consistency, solutions 3, 4, and 5 were discarded, shown as follows:

These equations are strongly recommended to avoid, because these combinations are key fail factors (KFF) in social impact startup (SIS).

Discussion

This paper contributes to the knowledge revealing the underlying variables through the key success factor (KSF) and its negation (~KSF) to get the key fail factors (KFF) for the SIS model, which was empirically proved in several stages (Mejía-Trejo, 2021). See Table 7.

Hence, we proceed to describe the factors based on the CB-SEM relevant loading factors >0.6*** for the 100/300 Mexican/American SIS cases. The CBA-SEM loading factor results (Table 2/Table 3) highlight the importance of the underlying variables as key success factors (KSF) of SIS described in importance order of loading factor shown as follows (see Table 8).

Based on Table 4 and Table 5, the explanation of variable combinations of each factor's comparison as KSF-SIS between Mexican/American SIS variables are displayed in: Table 9 for entrepreneur profile (EPR); Table 10 for market knowledge (MKK); Table 11 for strategic analysis (STA); Table 12 for key performance indicators (KPI); Table 13 for business plan (BPL); and Table 14 value proposition (VPN). All variables involved as a source of innovation strategies.

As we can see for the Mexican case (see Table 4), the H1, H2, and H6 hypotheses are rejected due to the low levels of their standardized path coefficient ẞ < 0.7 (0.608***; 0.650*** and 0.620 respectively). It is necessary to work on how to improve such path coefficients. Based on the fsQCA, when researchers allow for "equifinality" and "causal complexity" (Ragin, 1988), a common finding is that several different combinations of causal conditions may result in a given outcome. These combinations are, for the outcome, generally understood as alternate causal paths or "recipes". In this sense, we obtained prior "necessary conditions" measurements to proceed to get the "sufficiency conditions" with "coverage-consistency" to get the opposite outcome of the key success factors (~KSF) combination, in other words, the key fail factors (KFF); see Table 6 (Ragin, 2008; Mejía-Trejo, 2020). Hence, we have that H7 is accepted. Hence, we can affirm that there is no a single best combination, considered as key fail factors, that inhibit strategy business improvement for the next normal. Therefore, for Mexican SIS and eq.1, we have the final expressions:

These results correspond to the proposed theory as solution 1 is aimed to an absolute failure when there is a complete absence of the factors involved, affecting 99 percent cases of the Mexican SIS (see raw coverage in Table 4). Hence, we have:

For American sis and eq2:

The theoretical significance of this research comes from the novelty approach and methodology adopted and described above. Most of the SIS studies are variance-based methods that assume that the relationship is "symmetric" among variables. Indeed, the relationships among variables are relatively more "asymmetric". In other words: "High values of X are sufficient for high values of Y to occur, but high values of X are not necessary for high values of Y to occur. Hence, high values of Y occur when values of X are low indicating that additional causal recipes associate with high values of Y" (Fiss, 2011; Woodside, 2014).

The fsQCA is a method able to capture this asymmetry between SIS under an emergency context like COVID-19 pandemic ravages, involving entrepreneur profile (EPR), market knowledge (MKK), strategic analysis (STA), key performance indicators (KPI), business plan (BPL) and value proposition (VPN). These variables get different level combinations as key fail factors (KFF) for the SIS to create new hypotheses and theories when KSF fails (negated value ~KSF). The findings present intricate patterns among these factors and how their asymmetric relationships empirically determine the same outcome. Besides, this study contributes and extends the knowledge and comparative applications of the SEM and fsQCA to Mexico (as an emergent country), and the U.S. (a first-world country) aimed to explain several common conditions or relationships of the social impact startup (SIS), according to the special conditions of a specific country. Hence, our research's novelty is the combination of the factors identified in an empirical framework (Mejía-Trejo, 2021). Such framework describes, in principle, how these factors are related to getting high key success factors (KSF) in the SIS, and afterward how the same factors are related to getting just the opposite of KSF (~KSF), the key fail factors (KFF). The fsQCA uses on variables like entrepreneur profile (EPR), market knowledge (MKK), strategic analysis (STA), key performance indicators (KPI), business plan (BPL), and value proposition (VPN) represent a potential source of innovation strategies, by extension applied in design product/services, marketing business model, processes, organization, etc., and useful to the firms economically affected by emergency contexts like the COVID-19 pandemic in emergent and first world countries.

Practical Implications

Comparing Mexican SIS with American SIS despite the enormous difference in economy, public policies, education, etc., is a clear benchmark to follow to get and scale improvements for the Mexican SIS. There are a lot of lessons to learn. For instance, according to Table 2, for Mexican cases, it is necessary to work on how to improve the standardized path coefficients (ẞ) on key success factors (KSF) related with entrepreneur profile (EPR), market knowledge (MKK) and value proposition (VPN) to be comparable with the American side (see Table 3). According to Table 7 and Table 9, the creation of new SIS, particularly those that use technology and sustainable tenets based on their product or service, generates competitiveness and economic growth (Matson, 2006; UN, 2015). The SIS fail (or the negated key success factor, ~KSF in Table 4) so badly everywhere we look due to several causes, mainly, the allure of a good plan, a solid strategy, thorough market research, etc. (Eisenmann, 2021; Kasimov, 2017; Mahout and Lucas, 2017; Valencia, 2016; Deeb, 2013; Feinleib, 2012; Ries 2011; Skok, 2010). Due to the uncertainty, all of them must be judiciously analyzed and quickly applied (Ries, 2011; Pomerol, 2018). In an emergency context (like covid-19 pandemic), uncertainty boosts startup creation and development: "startups increase uncertainty and uncertainty encourages people to feed the process of startup creation" (Pomerol, 2018). Despite this, there is not enough information regarding the SIS in Mexico with the variables and indicators described here. Success is not delivering a feature; success is learning how to solve the customer's problem (Valencia, 2016). The research findings hroughout the Mexican SIS vs. American SIS comparisons, based on our KSF-SIS framework, provide useful implications for academics, business model innovation managers, and professional practitioners of innovation strategies. Suppose they use the conceptual model proposal implemented and proved in SIS under an emergency context (like covid-19) in an emergent country. Our model could obtain new insights on how the combinations of the variables (EPR, MKK, STA, BPL, KPI, and VPN) can be considered key success factors (KSF) to be analyzed in a broader strategic context, while, the opposite, the key fail factors (KFF), to be avoided.

Conclusions

This study verifies how events, like the covid-19 pandemic, are handled by 100/300 Mexican/American SIS survivors (in an emergent country and a first-world economy country) in the scenario of Jan-2021 to Jun-2021. These SIS had faced and handled the economic ravages, unemployment, competitiveness, productivity, and worse yet, the loss of the startup itself. Thereby, using CB-SEM in 100/300 Mexican/American SIS, we confirmed an empirical framework with 6 underlaying factors, 30 variables, and 30 indicators considered key success factors (KSF). We unveiled several essential issues, if we do not consider the different characteristics between countries, in number, size, activities, national economic policies, and consider all the results on the same level. Thereby, this framework allowed us to determine how Mexico's SIS must work in the development of several factors, primarily on the entrepreneur profile (EPR); the market knowledge (MKK), and the value proposition (VPN), in comparison to the American SIS. Although they are in acceptable levels the other factor values, such as strategic analysis (STA), key performance indicators (KPI), and business plan (BPL), must be improved adapting the EPR, MKK, and VPN factors. A complete analysis of each variable per factor is offered to appreciate the innovation strategies to be analyzed as a product of the unique results (KSF) of the Mexican SIS and American SIS. Besides, this framework allowed us to conclude that there is no single best combination of factors, considered key fail factors (KFF) that inhibit innovation strategies and must be avoided to improve Mexican SIS / American SIS for the next normal. In this sense, the novelty of this study is the analysis of the opposite KSF conditions to get the key fail factors (KFF) through the use of fsQCA. A complete analysis of each factor is offered to appreciate the innovation strategies to be avoided as a product of the combinations or path results (KFF) of the Mexican SIS and American SIS. fsQCA displays several different paths to get the same outcome, in this case, the KFF with necessary, sufficiency, and consistency conditions. Hence, for Mexican SIS 5 combinations of factor presence levels to be avoided were displayed, while for American SIS only 2 of such combinations of factor levels were determined. To determine each factor's presence level the application of the CB-SEM is suggested, which displays the values of each variable involved per factor.

For Mexican SIS, we obtained 5 useful patterns with the same outcome, the key fail factors (KFF) as Eq.1.

For American SIS, we obtained 5 useful patterns with the same outcome, the key fail factors (KFF) as Eq.2.

Finally, the combination of different levels of each of the six factors permits the strategist in several areas such as innovation, business, marketing, etc., to combine them to improve their strategy in the market.

Limitations and Future Studies

All empirical studies have some limitations. First, the industry's and the sectors of the sis' willingness to cooperate as sources of information. Not all of them are available to provide information under equal conditions and times. Second, the results consisted of a scale of self-reported data to remind their perceptions. Further studies could combine direct observations of specific sis with our scale with survey data from direct semi-structured interviews and from other emergent countries as well. Third, future research may also include other different factors, variables, or indicators as key success factors (KSF) in other kind of startups, or for instance, the influence of public policies, the grouping of CEOS by gender, education level, incomes level, key partners, funding resources, etc. which could offer more useful information.

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